What can large language models do for sustainable food?
Authors: Anna Thomas
Adam Yee
Andrew Mayne
Maya B. Mathur
Dan Jurafsky
Kristina Gligorić
Venue: ICML 2025 poster (2025)
Keywords: large language models
sustainability
climate
food
health
optimization
Primary area: applications
TL;DR: Motivated by food systems' major role in climate change, we define a typology of sustainable food development tasks, demonstrate expert-level LLM performance, and propose a novel framework integrating LLMs with combinatorial optimization.

Abstract: Food systems are responsible for a third of human-caused greenhouse gas emissions. We investigate what Large Language Models (LLMs) can contribute to reducing the environmental impacts of food production. We define a typology of design and prediction tasks based on the sustainable food literature and collaboration with domain experts, and evaluate six LLMs on four tasks in our typology. For example, for a sustainable protein design task, food science experts estimated that collaboration with an LLM can reduce time spent by 45% on average, compared to 22% for collaboration with another expert human food scientist. However, for a sustainable menu design task, LLMs produce suboptimal solutions when instructed to consider both human satisfaction and climate impacts. We propose a general framework for integrating LLMs with combinatorial optimization to improve reasoning capabilities. Our approach decreases emissions of food choices by 79% in a hypothetical restaurant while maintaining participants' satisfaction with their set of choices. Our results demonstrate LLMs' potential, supported by optimization techniques, to accelerate sustainable food development and adoption.

What Can Large Language Models Do for Sustainable Food?
Anna T. Thomas'
Adam Yee? Andrew Mayne'
Maya B. Mathur'
Dan Jurafsky Kristina Gligorié
Abstract
more sustainable protein sources (Xu al., 2021; Mylan et al., 2023). Additionally, programs such as the Menus of Change have developed to promote sustainable choices in foodservice operations (Menus of Change, 2024). These di- rections require knowledge of hoth food science and human preferences.
Food systems are responsible for third of human- caused greenhouse gas emissions. We investigate what Large Language Models (LLMs) can con- tribute to reducing the environmental impacts of food production. We define a typology of design and prediction tasks based on the sustainable food literature and collaboration with domain experts, and evaluate six LLMs four tasks in typol- ogy. For example, for sustainable protein design task, food science experts estimated that collabo- ration with an LLM can reduce time spent by 45% on average, compared to 22% for collaboration with another expert human food scientist. How- ever, for sustainable menu design task, LLMs produce suboptimal solutions when instructed to consider both human satisfaction and climate im- pacts. We propose a general framework for in- tegrating L.MSs with combinatorial optimization to improve reasoning capabilities. Our approach decreases emissions of food choices by 79% in hypothetical restaurant while maintaining par- ticipants' satisfaction with their set of choices. Our results demonstrate LLMs' potential, sup- ported by optimization techniques, to accelerate sustainable food development and adoption. Our code is available at https: /ttp://ggtttt/bt com/ t thomasat/ / 11mm- sustainable- food.
Though LLMs have shown promise in scientific discov- ery (Boiko et al., 2023; Guo et al., 2023) and modeling hu- man behavior (Park et al., 2022; Horton, 2023), applications in sustainable food remain underexplored. Beyond its im- portance in sustainability and public health, this domain rep- resents compelling testbed for broader machine learning (ML) challenges, including modeling diverse human pref- erences, generating creative solutions, and aligning outputs with real world constraints and societal values (Chakraborty et al., 2024; Yang al., 2024). These challenges not only central to sustainable food but also to other domains, making sustainable food tasks valuable benchmark for advancing general ML methods. To initiate an exploration of what LLMs can contribute to sustainable food, we assembled a team consisting of culinary and food science professionals and researchers in ML, natural language processing, and statistics. We make three main contributions:
Based on collaboration with expert culinary and food science professionals, we define a typology of tasks grounded in the needs of food scientists and chefs. Our typology covers design and prediction tasks across the resolutions of ingredients, recipes, and food systems.
We evaluate six LLMs on four concrete instantiations of tasks in our typology, incorporating two datasets not previously studied by the machine learning com- munity. Notably, LLMs meet or exceed expert human performance on our sustainable protein experimental design task, and also show promising performance in coarse caaseegrraieed preference prediction. However, they exhibit an omnivore bias, produce suboptimal solutions when balancing multiple constraints (e.g. maintain sat- isfaction while reducing emissions), and fail to perform well in fine-grained preference prediction.
1. Introduction
Global food production practices are a leading contributor to climate change, deforestation, water pollution, biodiversity loss, antibiotic resistance, and zoonotic disease transmis- sion (Allan al., 2023; Hopwood et al., 2020: Scherer et al., 2019). Given the disproportionate harms of animal agricul- ture in particular, which accounts for 57% of food systems emissions, number of companies have formed to develop
Stanford University Umai Works. Correspondence Anna Thomas <thomasat @eeinnssttssaaffrttt stanford.edux> Kristina Gligorić <glig- oric @ stanford.edu>>
We propose an approach that integrates LLMs with combinatorial optimization techniques to address LLMs' relative weakness in mathematical reasoning, and demonstrate that our approach achieves 79% av-
Proceedings of the 42nd International Conference Machine Learning. Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

---

What Can Large Language Models Do for Sustainable Food?
erage emissions reduction in food choices while main- taining patron satisfaction with their set of choices.
on the sustainable food literature and collaboration with ex- perts, we identify two types of tasks, (1) design and (2) pre- diction, across three resolutions, (I) ingredients, (2) recipes, and (3) systems. Design tasks focus on creating or opti- mizing food products or systems to meet specific target properties, such as sensory dimensions (taste, texture, etc.), nutrition, cost, and climate impacts. Prediction tasks focus on inferring the mapping from given representation of a food product or system to a property of interest. Ingredients are the fundamental components used to create food prod- ucts, including raw materials (e.g- grains, legumes, oils) and processed components (c.g. protein isolates, flavorings), Recipes are structured formulations or instructions for com- bining ingredients to create food products. Systems refer to the broader processes that govern the production, distribu- tion, and consumption of food. This includes supply chains, foodservice operations, and consumer behavior.
2. Related Work
Large Language Model Evaluations. Rigorous evaluation of LLMs has clarified their strengths and weaknesses. Guha et al. (2024) identified six types of legal reasoning tasks and evaluated 20 models on them. In chemistry, Guo al. (2023) identified three types of capabilities, established benchmark of eight tasks, and evaluated five LLMs. How- ever, no work, to our knowledge, has formalized key tasks and evaluated LLMs in the sustainable food domain.
Large Language Models for Scientific Discovery. In chemistry, Boiko al. (2023) demonstrated that GPT-4 com- bined with internet search and code execution can perform experimental design and execution. In genomics, Roohani et al. (2024) showed that an LLM with access to several tools can design genetic perturbation experiments. In com- putational nanobody design, Swanson et al. (2024) propose a framework in which LLM agents receive periodic human feedback, and showed promising performance in the design of novel antibodies. In artificial intelligence research, Si et al. (2024) show that LLMs can generate novel research ideas. However, we are not aware of prior work on LLMs for discovery of novel sustainable foods.
We additionally distinguish between novel and traditional foods. Novel sustainable foods, often referred to as "sus- tainable proteins" or "alternative proteins". are defined as foods developed using modern food science and technology to emulate or improve upon animal products with respect to dimensions such as taste. The three main types of sustain- able protein technologies are plant-based, cultivated, and fermentation (Good Food Institute, 2025). Here we focus on plant-based products. Example of novel plant-based based products include plant-based meat analogs (c.g. Beyond and Impossible burgers) and plant-based dairy alternatives (e.g. oat milk). While fermentation and cultivated meat are promising approaches for shifting consumption, and we believe our frameworks can be extended to these domains as well, they are outside the scope of the present work. We re- fer the reader to Waschulin & Specht (2018) and Todhunter et al. (2024) to learn about these two technologies.
Large Language Models for Simulating Human Prefer- ences. While we are not aware of prior work studying how LLMs can model human preferences and perceptions related to food, a growing literature has shown evidence that L.MMs can accurately simulate human behavior in tasks such as search behavior, participation in online communities, hiring scenarios, and classic economic, psycholinguistic, and so- cial psychology experiments (Zhang et al., 2024; Park et al., 2022; Horton, 2023; Aher et al., 2023).
3.2. Selected Tasks
Large Language Models and Optimization. Our work also pertains to the use of LLMs for mathematical optimiza- tion, and combining LLMs with optimization techniques. Yang et al. (2024) evaluated LLMss for several optimization problems. Ahmaditeshnizi et al. (2024) introduce OptiMUS, an LLMebased agent for solving optimization problems. Motivated by sustainable food applications, we propose a general framework for integrating LLMs with combinatorial optimization techniques that leverages LLMs' knowledge on topics such as human preferences. We further discuss these works and additional related work in Appendix B.
Starting from this general typology, we then selected four tasks to address in the context of both sustainable protein and traditional foods. These tasks include two design tasks (experimental design and menu design) and two prediction tasks (sensory profile prediction and recipe preference pre- diction). The four tasks were selected based interviews with food scientists and chefs.
Experimental Design. Taste is key determinant of food choice (Glanz et al., 1998). Food companies depend on "sensory panels", a group of individuals who provide feed- back on food, to routinely quantify sensory properties of their products (Kerth, 2013). Once the sensory panel evalu- ation has been run, food companies will then synthesize the feedback and revise their product accordingly via series of experiments (Beckley et al., 2008; Tomsov, 2023). We consider the common task of, given a product formulation
3. Task Definitions
3.1. Typology
We identify a non-exhaustive typology of sustainable food tasks amenable to ML approaches, shown in Table 1. Based
2

---

What Can Large Language Models Do for Sustainable Food?
Task |Resolution
Ingredients
Recipes
Systems
1. Design breeding strategies for chickpeas to improve yield
1. Design a lentil soup recipe meet- ing nutritional and cost constraints
Design a menu meeting a food-
Design
service operation's 's constraints 2. Design an optimized supply chain for a plant-based meat com- pany to reduce climate impacts
2. Design a plant-based protein blend to mimic egg white texture
2. Design experiments to improve plant- bast-based product formulation in response to feedback
1. Predict complementary ingre- dient pairings for traditional plant proteins
1. Predict which of two recipes target population will prefer
Predict change in patron satis- faction when making plant- hased the default
Prediction
2. Predict functional properties (e.g.. solubility) of a proposed plant-based analog for casein
2. Predict sensory profile of plant-hased product formulation
2. Predict climate and economic impacts of regional transition to plant- based meat
Table I. A typology of tasks at the intersection of ML. and sustainable food development. Tasks are categorized into design and prediction across the resolutions of ingredients, recipes, and food systems. For each task, we list examples corresponding to both traditional (I) and novel (2) foods. Tasks we study are highlighted.
and sensory panel feedback, designing set of experiments to address the identified issues and improve the product.
4. Datasets
NECTAR Sustainable Protein Dataset. The NECTAR Initiative's (nectar.. org) sensory panel data' is freely available to academic researchers. The dataset, which will continue to expand in size, consists of 47 products across five categories. One product in each category is a meat- based reference product, and one product is a hybrid beef and mushroom burger, yielding 41 plant-based products. For each product, least 100 sensory evaluations from Amer- ican omnivores were performed along 21 quantitative and qualitative dimensions. More details are in Appendix C.I.
Menu Design. Foodservice operations, such as restaurants and dining halls, aiming to reduce their climate impacts must also balance other factors, such as patron satisfaction, nutrition, and cost. We thus consider the task of revising menu to promote more sustainable choices while maintain- ing patron satisfaction and other relevant factors (Banerjee et al., 2023; Parkin & Attwood, 2022).
Sensory Profile Prediction. Sensory panel evaluations are very expensive and time consuming to run, and smaller food companies usually cannot afford to run them (Varela & Ares, 2012). Due to these challenges, we consider the task of estimating sustainable protein product's sensory profile. While we do not expect to eliminate the need for sensory panels altogether, accurate sensory modeling could reduce the need for experiments and help to efficiently prioritize them. We note that prior work has shown that ML can accurately model the human sense of smell (Lee et al., 2023), which is closely related to taste (Spence, 2015).
Food.com Recipe Dataset. The Food. com dataset² con- tains 522,517 recipes, including ingredients and preparation instructions. We use the associated 1,401,982 reviews, con- taining ratings and text, to capture online users' preferences. Given the large number of recipes, the dataset allows for finding pairs of comparable recipes, i.e., similar in dish type and ingredients, but significantly differing in the users's s ratings. An example recipe is shown in Appendix C.2.
Recipe Preference Prediction. Similarly, in the domain of traditional foods, chefs must anticipate patron prefer- ences and design their recipes and menus accordingly. We thus consider the task of predicting the mapping from a traditional recipe to average consumer satisfaction within specified population. We study how accuracy on this task compares for plant-based versus animal-based recipes. While food preferences of course vary greatly from individ- ual to individual, in both of our prediction tasks the goal is to simulate representative sample of humans from a specified population (Aher et al., 2023).
5. Experimental Evaluation
We evaluate Claude 3.5 Sonnet, Gemini 1.5 Pro, GPT-3.5 Turbo, GPT-4o, Llama 3.1 70b Instruct, and ol o1-preview. In the experimental design task, due to limited availability of expert food scientists, we used ol o1-preview only, based on feedback on relative LLM performance from our food scientist team member. We use a combination of automated evaluations and human subjects evaluations, including both food science experts and participants recruited on Prolific. IRB approval was obtained. All evaluations were in a zero- shot setting. Additional details for each task, including prompts and additional results, are in Appendices D and E.
'Access can be requested here. PPbllicyy available here.
3

---

What Can Large Language Models Do for Sustainable Food?
5.1. Experimental Design (Sustainable Protein)
final approach, described in Section 6, can also easily be ex- tended other constraints such nutrition, cost, allergies, other dimensions of climate impact, etc.
Methods. In this task we study whether LLMs can gener- ate experimental design for systematically improving sustainable protein product on the basis of qualitative and quantitative sensory panel data. We evaluate the perfor- mance of ol-preview via the feedback of 20 expert food scientists, with an average of five years of experience in plant-based food science and three years of experience in plant-based meat specifically. In Phase 1, we ask both ol- preview and expert food scientists to generate experimental designs. In Phase 2, we ask food scientists to evaluate both ol -preview and a fellow food scientist (blinded, and in randomized order) along the dimensions of accuracy, speci- ficity, complementarity (to the evaluating food scientist's own thought process), and estimated time saved by collabo- rating with the anonymous "scientist." We ensured that no food scientists evaluated their own ideas. The food scien- tists were compensated via $50 Amazon gift cards, and additional $50 bonus was provided to the highest scoring food scientist from Phase to incentivize effort. Before Phase 2. following Si et al. (2024) we perform style stan- dardization (also with -preevve) on both the LLM and human responses to avoid confounding by style. In order to approximately match the length of the human responses, ol ol-preview was instructed to limit its response to 250 words.
Comparison Human (Expert Food Scientist) and LLM Performance
0.8
0.6
Scares
0.2
0.0
Accurany
Specificty
Complemenianty
Parcent Time Saved
Expert Food Scientist
LLM (o1-preview)
Figure 1. ol -preview meets exceeds the performance of expert food scientists on a sustainable protein design task. ol -reeevvew and food scientists were asked to generate experimental designs for improving product formulations in response to sensory panel feed- back, common task for food scientists. Error bars 95% confidence intervals. Example outputs are in Appendix E.
Results. Our results are shown in Figure 1. Across the 30 products, the mean performance of LLMS was higher on all four dimensions, though only statistically significant (in a t-test) for specificity (p=0.003) and percent time saved (p=0.0002). Mean estimated percent time saved was 22% for the human food scientists and 45% for ol-preview.
As a reference menu, we use a 36-item menu from Banerjee et al. (2023), created using Deliveroo's and Just Eat's top 100 items ordered in the United Kingdom in 2019. Each item consists of title and an appealing description, e.g. "Chicken Curry Ramen. Japanese fried chicken and noodles in delicious curry broth." Each LLM was instructed to reduce emissions of food choices by 75% while maintaining satisfaction, cost, nutrition, preparation time, and animal welfare, and using only the same ingredients as the original menu other than standard vegetarian protein sources (most of which were already in the original menu, other than eggs and chickpeas). Adherence to the ingredient availability constraint, as well as meeting the constraint of generating 36 properly formatted recipes, was checked, and the L.M was given up to five chances to correct itself. GPT-3.5 Turbo and Llama 3.1 70b Instruct did not meet the formatting or ingredient constraints.
5.2. Menu Design (Traditional Foods)
Methods. Here we study what LLMs can contribute to greater adoption of existing sustainable foods. Extensive past work has studied how to shift consumption toward sus- tainable options in foodservice operations (Lohmann et al., 2024; Attwood et al., 2020; Banerjee et al., 2023; Weijers et al., 2024). However, these studies either obtained rel- atively small improvements in climate impact or did not measure patron satisfaction with their set of choices. Ad- ditionally, none accounted for the trade-off known as the "small body problem", in which simply substituting beef by chicken fish, common approach for reducing cli- mate impacts, can be much worse for animal welfare due to the relative sizes of these animals and their typical condi- tions (Mathur, 2022). Public opinion polling suggests high levels of concern for animal welfare (Gallup, 2015). Given that LLMs' training data includes many recipes and menus, explore what L.LMs contribute to designing appeal- ing menus under the constraints of ingredient availability, greenhouse gas (GHG) emissions, and animal welfare. Our
Results. We found that this approach yielded entirely veg- etarian or vegan menus (shown in Appendix F), known to generally decrease satisfaction in a typical omnivore pop- ulation (Hartmann & Siegrist, 2017). This decrease in sat- isfaction was further supported in our later experiments (Figure 2), motivating our approach described in Section

---

What Can Large Language Models Do for Sustainable Food?
Claude 3.5 Sonnet
Gemini 1.5 Pro
GPT-3.5 Turbo
GPT-4o
Llama
ol preeviiw
Baseline
Sensory Profile Prediction: LLM Prediction vs. Sensory Panel (Sustainable Protein)
All Dimensions
0.61
0.53
0.52
0.59
0.57
0.64
0.67
Overall Satisfaction
0.60
0.41
0,38
0.39
0.51
0.64
0.73
Meatiness
0.61
0,57
0,51
0.62
0.52
0,66
0.48
Greasiness
0,58
0.64
0,63
0.74
0.73
0.64
0.69
Juiciness
0.62
0.60
0,63
0.63
0.51
0.64
0.71
Sweetness
0.36
0.61
0,61
0.61
0.50
0.64
0.75
Saltiness
0.70
0.67
0.57
0.73
0.70
0.75
0.73
Purchase
0.65
0.29
0.37
0,45
0.43
0.54
0.61
Recipe Preference Prediction (Food. com):
LLM Prediction vs. Rating (Traditional Foods)
Rating
0.63
0.48
0.57
0.64
0.51
0.60
0.50
Recipe Preference Prediction (Menu Design): LLM Prediction vs. Order Frequency (Traditional Foods)
Order Frequency
0.73
0.62
0.67
0.60
0.56
0.50
Table We evaluate LLMs' pairwise ranking accuracy for three forms preference prediction: 1) the mapping from a sustainable protein product's s ingredients and nutritional information to sensory properties 2) the mapping from recipe to its online rating 3) the mapping from recipe to frequency orders a hypothetical restaurant. Sample sizes are in Appendix Table 9. Statistically significant results, based on a chi-squared test, are in bold. For the sustainable protein task, the baseline is described in Section 5.3. For recipe preference prediction, the baseline corresponds to random choice. GPT-3.5 Turbo did not produce a valid output in the menu design experiment.
5.3. Sensory Profile Prediction (Sustainable Protein)
to 81% and 75% for overall satisfaction and purchase intent. However, it is still not mcaningfully superior to the simple baseline, which achieved 86% and 70% accuracy on those dimensions in this subset of pairs (Appendix Table 18). An expert human baseline for the "Overall Satisfaction" dimen- sion was collected from collaborator with deep domain expertise (11 (11 years of food science experience, including co- fo-founding two plant-based bastd meat companies), who is also a co-author on this paper. The results are shown in Appendix Table 14.
Methods. Here we study whether LLMs can make ac- curate predictions about the mapping from a sustainable protein product's ingredients and nutritional information to its sensory properties. Of the 21 sensory dimensions in the NECTAR dataset, seven dimensions (overall satisfaction, meatiness, greasiness, sweetness, juiciness, saltiness, pur- chase intent) were selected for testing, to reduce the number of tests. Within each of the five product categories, and each sensory dimension, we ask LLMs to compare pairs (with randomized ordering, to account for positional bi- ases) of plant- products with a statistically significant (p 0.05 in a (-test) difference on the sensory dimension, as evaluated by human omnivores. This yielded 495 pairs. For the baseline, we use the available nutritional informa- tion and prior knowledge from the food science literature. Specifically, for greasiness and juiciness, meatiness, sweet- ness, and saltiness, the ratio total fat, protein, sugar, and sodium respectively per serving to serving size are used to predict the sensory dimension. For example, the product that is higher on protein is ranked higher on meatiness cording to the baseline. For overall satisfaction and purchase intent, the average of normalized fat and sodium content is used (Drewnowski & Greenwood, 1983; Mattes, 1997).
5.4. Recipe Preference Prediction (Traditional Foods)
Methods. We created a set of 500 recipe pairs with sim- ilar ingredients but significantly different average ratings (p < 0.05 in a t-test), ensuring that one recipe consis- tently preferred by users. Examples pairs are shown in Appendix C.2. To identifyy recipe pairs with the highest similarity, the overlap of ingredients was calculated for each pair using the ratio of shared ingredients to the total number of unique ingredients. We addressed the possibility of over- lap with LLM training data by curating an additional set of recipe pairs from ep curi ous com. These recipes were posted behind a paywall after the release dates of all the models we tested. Our primary metric was accuracy- whether the models correctly identified the preferred recipe- compared to a random baseline. We tested statistical signifi- cance using a chi-squared test with Bonferroni correction when evaluating on multiple conditions. The order in which the recipes were shown was randomized.
Results. Accuracies are shown in Table 2. Accuracies significant according to chi-squared test with Bonferroni correction for multiple testing are bolded (VenderWeele & Mathur, 2019). Performance is generally not meaningfully superior to the baseline. Accuracy for the best (across all dimensions) LLM, ol -preview, improves in the highest quar- tile of the ground truth difference between the recipes, e.g.
Results. Claude 3.5 Sonnet, GPT-40, and ol- preview outperform the random baseline (Table 2). The
5

---

max f(x) What Can Large Language Models Do for Sustainable Food?
highest accuracy achieved is 64% (GPT-4o), with accuracy improving 85% in the highest quartile of the ground truth review score gap, i.e., pairs of recipes for which peo- ple's preferences were most different (Appendix Table 19). Moreover, performance varies widely by comparison type (Appendix Table 20). Across all the models, accuracy is the highest when two non-vegetarian meals are compared, followed by vegetarian vs. non-vegetarian comparisons, with the worst accuracy observed in vegetarian ian meal comparisons. This aligns with existing literature showing that minority preferences and opinions are mod- eled less accurately (Santurkar et al., 2023). Within the vegetarian vs. non-vegetarian meal comparisons, accuracy lower when vegetarian options are rated higher, reflect- ing LLMs' omnivore bias, as LLMs more frequently prefer non-vegetarian options compared to ground truth human preferences. This finding is consistent with the literature on bias amplification in the food domain (Luo et al., 2024) and points to the need to improve preference prediction per- formance in order to develop tools that can better support the development of sustainable food. Finally, similar pair- wise accuracy across models on post-cut-off data suggests that performance cannot be fully attributed memorization (Appendix Table 21).
optimization. While LLMs alone struggle with optimization, and traditional optimization methods do not have access to LLMs' background knowledge, combining them allows for leveraging the strengths of both approaches.
6.1. LLM-Guided Combinatorial Optimization
We begin with the general constrained optimization setting:
vegetar-
x
(I)
s.t. (lv)s [L...,,
We make three assumptions:
1. The optimization problem involves selecting a subset of items S from a ground set U = { [ui,- ..... uN} Each u; represents an item, c.g. a recipe or exercise. The selection is represented by a binary vector x E {0, 13N, where x; = 1 if u; is selected, and 0 otherwise.
2. At least one of f(x:) or the constraints g (x) can be expressed in terms of scoring function p(us). which computes scalar property (e.g, satisfaction) for each item ug. The scoring function can be estimated by an LLM. For example, if the objective is to maximize the total satisfaction of the end user with the selected items, f (xr) can be written as fxx Σi p(u.).,,
6. Augmenting LLMs with Combinatorial Optimization
3. Once p(u:) is estimated for each u, in U, (1) has tractable form and can be solved exactly or approxi- mately via standard techniques, e.g. submodular opti- mization or integer programming for small to medium problem sizes (Fujishige, 2005; Wolsey, 2020). We give a concrete example in Section 6.2.
When prompted directly to revise menu to reduce emis- sions by at least 75% while maintaining patron satisfac- tion and other factors, LLMs generate entirely plant-based menus (Section 5.2, Appendix F). While this approach achieves the emissions target, it significantly decreases sat- isfaction (Figure 2). This failure highlights a broader, pre- viously documented challenge: LLMs struggle with opti- mization problems (Ahmaditeshnizi et al., 2024; Yang et al., 2024). However, firequetly in real-world applications we need to consider some subjective, -umannevaluated prop- erty (like satisfaction) while also satisfying other constraints (like emissions targets or budgets). For instance, in educa- tion, we may want to maximize student engagement while meeting curriculum requirements. In fitness, we may aim to maximize enjoyment while covering specified muscle groups within time constraints. In travel planning, we may seek to maximize trip enjoyment while meeting budget and scheduling constraints. Additionally, in some settings it may take considerable effort to quantify dimensions such as emissions or other climate impacts of a food item.
We can then solve (1) via the following steps:
1. Generate the ground set U {ui, .... uN), e.g. diverse set of recipes or exercises.
2. Obtain the estimates p (4,) Vi E [1,..., N] via an LLM.
Solve the combinatorial optimization problem using standard techniques, e.g. submodular optimization or integer programming, depending on the forms of f(x) and gi (3r). This will yield a subset S C C.
Step could be performed via an LLM or via other tech- niques. We discuss the relationship of this framework to prior work in Appendix B. A natural question is how per- formance depends on the accuracy of the LLM's ss estimates p(u.). and other parameters. For our analysis we consider the cardinality constrained setting where f(r) has compo- nent that is linear in p(u,) for the selected subset, and an- other component d(z)) that does not depend on p(u.). Con- cretely, we aim to maximize f(z) Σ p(u:)(()))))))( + Ad(x)) subject to the constraint Σ Iz = K, though this result can be extended to more general objectives and constraints.
We propose to address these challenges by combining LLMs' background knowledge and ability to model hu- man preferences, demonstrated in Horton (2023), Park et al. (2022) and in our own experiments, particularly for coarse- grained prediction (Tables 2, 2.11. 1,18,19), with mathematical
6

---

What Can Large Language Models Do for Sustainable Food?
Proposition 6.1. Let x² be the optimal solution with re- spect actual preferences p(u,), and &* be the optimal solution with respect to estimated preferences f(u,). Let p(u,), ((u,)) E [0, 1). d(x)) E [0. D). ff(((u,) - p(u,) S for all Us E U. then [f(*) - [x=*)] S 2Ke.
and 1.0 for animal welfare, corresponding to reducing emis- sions by at least 75% while maintaining or improving upon animal welfare. We set K = 36, to match the length of the original menu from Banerjee et al. (2023). We prompt the L.M to generate 20 additional recipes. Thus, N = 56. Finally, we set À = 100. For computing pairwise recipe similarity we use the Ratelif/OObershelp sequence match- ing algorithm as implemented in Python's 's difflib (Ratcliff et al., 1988). The recipes were ordered first by whether they were LLM generated, and second by predicted rating. We included baseline in which the original menu was simply re-ordered so that vegetarian items were first, to account for the possibility that benefits of the LLM+IQP approach were simply due to placing the LLM generated (and therefore plant-based) recipes first.
A simple proof is in Appendix A. Thus, the error can be bounded as a linear function of the number of items selected and the maximum item-level error of the LLM.
6.2. Constrained Menu Design Formulation
Now we show how to implement this framework for the setting of constrained menu design. First, we prompt the L.MM to generate a set of plant-based candidate recipes, though this could be modified to allow for also generat- ing animal-based recipes this stage. As in Section 5.2, the LLM is also instructed to not worsen cost, nutrition, or preparation time. We then combine this set with the recipes in the original menu O where K = |0), to form the ground set U = C O. We then estimate p(u+) Vu, E U by prompting an LLM to rate each recipe title in the ground set by expected preferences of the population of interest (here, American omnivores). Then, integer quadratic pro- gramming (IQP) is applied to select subset of recipes from U for the menu.
We then evaluated our approach via two human subjects experiments (total n = 800) in which participants recruited via Prolific, constrained to be in the United States and flu- ent English speakers, were instructed to make a selection from a menu. In the first experiment, the menu was ran- domly assigned from one of the five LLMS (augmented with IQP) and three baselines (original menu, original menu with beef removed, and a vegetarian subset of the original menu).² 3 In the second experiment, conducted the following day, the menu was randomly assigned from one of three additional baselines (ol-preview on its own, without IQP; ol prreview+IQP, but with the ol -previiww generated descrip- tions replaced with a simple list of ingredients; and placing the vegetarian items first in the original menu). The par- ticipants were paid $16 per hour. The evaluation metries consisted of satisfaction metrics (satisfaction with their set of choices, likelihood of visiting, and likelihood of rec- ommending the restaurant to friend), emissions of food choices, and animal welfare impacts of food choices.
More specifically, we maximize expected satisfaction with the selected recipes, plus a Ad(x)) term, where À controls the weight on diversity, to encourage a diverse set of recipes, subject to constraints on climate impacts and animal welfare. This approach easily extends to other constraints such nutrition and cost. The predicted preferences p(u,) used to compute expected emissions and animal welfare impacts of choices by weighting more preferred options more highly, under the assumption that these options will be chosen more often. Our problem formulation is thus as follows:
6.3. Results
N
The results for the best LLM, ol ol-preview, are shown in Figure 2, with results for other I.LMs and baselines in Ap- pendix E.2. When controlling for dietary preference, age, gender, and race, we estimate that the ol or-review++IQP ap- proach reduces emissions by 79% while maintaining patron satisfaction with their set of choices (p=1.37e-18) We ob- tain similar results when removing vegan and vegetarian participants (Appendix E.2). We observe a small (5%) re- duction in the likelihood of recommending the restaurant to a friend, and a small increase in animal usage (3.5%), but these were not statistically significant. The original, baseline, and generated menus are shown in Appendix F, We also evaluated the accuracy of the LLM's predicted pref- erence ratings, in the same pairwise ranking fashion as the
max
Σ
p(u)) ; + Ad(x)
1=1
s.t.
Ex]((()) S (C
Σ m== m==K
24 E (0. 1 E N]
To corresponds to the subset of recipes in the original menu. EpIt, [x)] computes the expected emissions or animal wel- fare impacts of the chosen subset of recipes, with respect to the probability distribution associated with ((u:).
We set d(x) to be negative pairwise similarity between the recipes in the selected subset. This yields an IQP since all other terms are linear in x. C2 controls the reduction in
'Removal of beef a common strategy for reducing climate impacts (Grummon et al., 2023).
emissions or animal welfare, and is set to 0.25 for emissions
7

---

What Can Large Language Models Do for Sustainable Food?
Emissions
Satisfaction
Emissions (kg COgeq per og food Item
70
12
10
60-
Sattsfaction
40-
20
0
Orginal o1-preveww1IPP Ablation:
Baseline:
Baseline:
Original al preveewwllPP Ablation:
Baseline: Vegelatian Remove Beel
Baseline:
01-preview
Vegetarian Remove Beel
01-preview
Figure Our ol --review++QP approach reduces GHG emissions of food choices by 79% compared to the original menu from Banerjec al. (2023) while maintaining participants' satisfaction with their set of choices. ol ol-preview+IQP additionally oulperforms the baselines of removing meat from the original menu ("Vegetarian") and removing beef ("Remove Beef"), and maintains animal welfare and other satisfaction metries, as shown in Appendix F.2. As ablation we also prompt ol -pervvee directly to revise the menu. Error bars are 95% confidence intervals. Statistical significance was assessed with a f-test, with Bonferroni correction for multiple hypothesis testing.
sensory profile and recipe preference prediction tasks, with the best LLM, Claude 3.5 Sonnet, achieving 73% accuracy (Table 2). This increases to 85% in the highest quartile of the preference gap (Appendix Table 11). An expert human baseline was collected from collaborator with deep domain expertise (32 years of culinary experience), who is also co-author on this paper. The results are shown in Appendix Table 13.
The second is the Food. com recipe rating task, where we compared LLM predictions to online recipe ratings. The third recipe rating in the context of the menu design exper- iment, where the LLM's recipe-level predictions are com- pared against the order frequency the frequency with which people chose the dishes in human subjects experiment for the recipes that were selected for the final menu. For the holistic measures of overall satisfaction and purchase intent in the sensory profile prediction task, no LLMs outperform random baseline, though some do on specific dimensions such as greasiness or saltiness. For online recipe rating, three of six LLMs outperform random baseline. All evalu- ated LLMs outperform a random bascline in context of the menu design experiment, where no actual tasting occurs and the orders are purely based on the text of the recipe (Table 2). Additionally, we studied how performance varies with the magnitude of the ground truth preference gap. When restrict- ing the evaluated recipe pairs be in the top quartile of preference gaps, LLM performance improves, with the best LLMS achieving 81%, 86%, and 85% accuracy respectively in the three prediction tasks (Appendix Tables 11,18,19). Thus, our analysis suggests that LLMs can be useful for coarse -rarse-grained preference prediction tasks, but that further work is needed for fine-grained prediction, particularly for the mapping from recipes or product formulations to actual taste. Additionally, consistent with past research on LLMs' modeling of minority preferences (Santurkar al., 2023), our results showed that LLMs model human preferences related to plant- based food less accurately. Thus, regular human feedback, e.g. taste testings, from diverse popula- tions remains critical. Overall, these results suggest that LLMs can be useful in experimental design, generation of sustainable recipes (as evaluated in the menu design task), and -rarse-graieed preference prediction.
7. Discussion
Our results identify both strengths and weaknesses of LLMs in the domain of sustainable food. One actionable finding is that, as determined by expert food scientists, ol o1-preview appears to be useful, even superior to fellow expert human food scientists, in generating ideas for revision of sustain- able protein products in response to sensory panel feedback. Our evaluation suggests that the benefits may be driven by increased specificity in particular, relative the human food scientists (Figure 1). L.Ms display weakness in the menu design task, where they are instructed to satisfy multiple constraints. They generate fully plant- plant-based based menus, which we find to significantly reduce satisfaction. We remedy this by integrating LLMs with combinatorial optimization tech- niques. Our approach achieves a 79% emissions reduction while maintaining patron satisfaction (Figure 2). Our work also contributes to the literature reasoning in L.M--baed systems (further diseussed in Appendix B), providing approach for solving a broad class of optimization problems that cannot be easily specified mathematically, c.g. those involving human preferences. Future work can apply this framework to other domains such as education and health.
We evaluated LLMs on three types of preference prediction. The first is sensory profile prediction, in which we compared LLM predictions to evaluations from an actual taste tasting.
Limitations. We did not do significant prompt engineering,
8

---

What Can Large Language Models Do for Sustainable Food?
and leave this as an area for future work. In the sustainable protein tasks, did not have access to the full product formulation, only ingredients and nutritional information. However, we consider this a positive, that users do not necessarily have to provide sensitive intellectual property to receive useful outputs from LLMs. In our menu design task, we did not provide incentives to make a realistic choice, such as delivering the meals to participants, Additionally, we did not study dimensions of food choice such as food waste and portion size. A stronger baseline for this task would have been human chef given the same menu and constraints, though we note that our "Vegetarian" baseline is based on the top vegetarian meals on online delivery apps. Future work could establish an expert human (food scientist or chef) baseline for the preference prediction tasks. In the experimental design task, implementing the suggestions and running second sensory panel to determine whether the suggestions improve the product is an area for future work.
Use of LLMs for developing unhealthy or less sustainable foods is also concern. We acknowledge that such work is likely ongoing, and we study here how LLMs can also be useful for applications that benefit society.
Lastly, the benefits of L.M--upporred development of sus- tainable food should be interpreted jointly with the environ- mental impacts of LLMs themselves (such as energy and water use). While pre-training in particular requires signifi- cant resources (Van Wynsberghe, 2021), we note that work to reduce the climate impacts of LLMs is ongoing (Patterson et al., 2021: Li et al., 2024). Future research can continue to explore ways to leverage pre-trained models for applications that contribute to positive environmental outcomes.
Acknowledgments
This work is supported by the Stanford Plant- Bastd Diet Initiative (PBDI). Anna Thomas is supported by National In- stitutes of Health Grant ROILM013866. Kristina Gligorié is supported by the Swiss National Science Foundation (Grant P500PT-21 127). We thank Caroline Cotto and Max Elder for access to the NECTAR dataset, and Moses Charikar, Ali Teshnizi, Madeleine Udell, and Katie Yoon for helpful dis- cussions. We thank Yifan Mai and the Center for Research on Foundation Models (CRFM) for their support.
Impact Statement
Our paper is directly motivated by broader societal impacts, specifically in sustainability and public health. Our task def- initions and results could spur progress in both development of novel sustainable foods and greater adoption of existing sustainable foods. The potential benefits of a sustainable protein transition are significant. For example, a recent life cycle assessment found that, relative to a beef burger patty, the production of a plant-based burger patty made with soy protein associated with 98%, 87%, and 99% lower GHG emissions, land use, and air pollution respectively (Saerens et al., 2021), with similar estimates for other types of sus- tainable protein (Good Food Institute, 2023), Additionally, the Intergovermmental Panel on Climate Change (IPCC) es- timates that a societal transition from animal-basee food (ABF) to plant-based food (PBF) by 2050 would reduce land-based GHG emissions by two-thirds relative the business-as-usie case (Intergovermenntal Panel on Climate Change, 2019).
References
Aher, G. V., Arriaga, R. I., and Kalai, A. T. Using large lan- guage models to simulate multiple humans and replicate human subject studies. In International Conference on Machine Learning, pp. 337-371. PMLR, 2023.
Ahmaditeshnizi, A.,, Gao, W., and Udell, M. OptiMUS: Scal- able optimization modeling with (MI)LP solvers and large language models. In Salakhutdinov, R., Kolter, Z. Heller, K., Weller, A., Oliver, N., Scarlett, J., and Berkenkamp, F. (eds.), Proceedings of the 41st International Conference on Machine Learning, volume 235 of Proceedings of Ma- chine Learning Research, pp. 577-596. PMLR, 21-27 Jul mlr press/
In the menu design task, we consider ethical aspects such as the trade-off's among patron satisfaction, sustainability, and animal welfare, and show that our approach can improve or maintain all dimensions. Designing menus to shift the distribution of patron choices may raise questions of respect for individual autonomy. We thus use three different metrics of patron satisfaction, and show that all are maintained with the revised menu (Appendix Figures 1 17,18.19).
2024. WRLLtttps://ppoceeete v235 /at.aaattssniii242.. html.
Allan, R. P., Arias, P. A., Berger, S., Canadell, J. G., Cassou, C., Chen, D., Cherchi, A., Connors, S. L.., Coppola, E., Cruz, F. A., et al. Intergovernmental Panel on Climate Change (IPCC) Summary for Policymakers. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, pp. 3-32. Cambridge University Press, 2023.
We also note that current sustainable proteins are sometimes considered "ultra- protess-r and therefore unhealthy (The New York Times, 2019). We view this as a challenge for the next generation of sustainable proteins that Al could potentially help with, via jointly optimizing for satisfaction, nutrition, climate impacts, cost, and other relevant factors.
A Attwood, S., Chesworth, S., and Parkin, B. Menu engineer- ing to encourage sustainable food choices when dining

---

What Can Large Language Models Do for Sustainable Food?
out: an online trial of priced-based decoys. Appetite, 149: 104601, 2020.
Computational Linguistics; Proceedings of the Main Con- ference, pp. 236-243, 2007.
Banerjee, S., Galizzi, M. M., John, P., and Mourato, S. Sustainable dietary choices improved by reflection before nudge an online experiment. Nature Sustainability, (12):1632-1142, 2023.
Drewnowski, A. and Greenwood, M. Cream and sugar: human preferences for high-fat foods. Physiology & behavior, 30(4):629 603, 1983.
Elmachtoub, A. N. and Grigas, P. Smart "predict, then optimize". Management Science, 68(1):9-26, 2022.
Beckley, J. H., Foley, M. M., Topp, E. J., Huang, J. C.,, and Prinyawiwatkul, W. Accelerating new food product design and development. John Wiley & Sons, 2008.
Faunalytics. The Impact Of Replacing An- imal Products Faunalytics ics.org. animal-product-impee scales/, 2020. [Accessed 21-05-2024).
faunalyt- org/
Bertsimas, D. and Kallus, N. From predictive prescriptive analytics. Management Science, (663):1025-1044 2020.
Boiko, D. A., MacKnight, R., Kline, B., and Gomes, G. Au- tonomous chemical research with large language models. Nature, 624(7992):570-578 2023.
Fujishige, S. Submodular functions and optimization. Else- vier, 2005.
Bulian, J.. Schäfer, M. S., Amini, A., Lam, H., Cia- ramita, M., Gaiarin, B.. Chen Huebscher, M., Buck, C., Mede, N. G., Leippold, M., and Strauss, N. As- sessing large language models on climate information. In Salakhutdinov, R., Kolter, Z., Heller, K., Weller, A., Oliver, N., Scarlett, J., and Berkenkamp, (eds.). Pro- ceedings of the 4/st International Conference on Ma- chine Learning, volume 235 of Proceedings of Machine Learning Research, pp. 4884-4935. PMLR, 21-27 Jul 2024. URL http::::: pttpppeiicgggss.epoopppppppp.ssp malr.press/ v235/bulian244 html.
Gallup, In U.S., More Say Animals Should Have Same Rights People news.gallupp.om https://news: http::////wws.aalpppp...pmp gallup..lpp.... com cwm www.poo1//m3275.1///1
aspx, 2015.
[Accessed 30-12-2024).
Glanz, K., Basil, M., Maibach, E., Goldherg, J., and Snyder, D. Why americans eat what they do: taste, nutrition, cost, convenience, and weight control concerns as influences on food consumption. Journal of the American Dietetic Association, 118-::16181166,11111111111111111 1998.
Good Food Institute. Environmental impacts of alter- native proteins. https: wwtp/rgoorgreeeeeeooo/ooooooeeee environmental-inpaede of-alternati wtt.eeennttiieepoteeeiisseseee/e 2023. [Accessed 18-12-2024].
Chakraborty, S., Qiu, J., Yuan, H., Koppel, A., Manocha, D., Huang, F., Bedi, A., and Wang, M. MaxMin- RI.HF: Alignment with diverse human preferences. In Salakhutdinov, R., Kolter, Z., Heller, K., Weller, A., Oliver, N., Scarlett, J., and Berkenkamp. F. (eds.), Pro- ceedings of the 41st International Conference on Ma- chine Learning, volume 235 of Proceedings of Machine Learning Research, pp. 6116-6135. PMLR, 21-27 Jul 2024. URL /tlLattps://rrccedinng mllr..r...... pprss// v235 /335/chaze 3.35/chakraborty2244 html.
Good Food Institute. Science. htttp:://ff.org science/, 2025. [Accessed 06-01-2025).
Grummon, A. H., Lee, C. J., Robinson, T. N., Rimm, E. B., and Rose, D. Simple dietary substitutions reduce carbon footprints and improve dietary quality across di- verse segments of the us population. Nature Food, 4(1 I): 966-977, 2023.
Chapman, M., Goldstein, B. R., Schell, C. J., Brashares, S., Carter, N. H., Ellis-Soto, D., Faxon, H. O., Gold- stein, J. E., Halpern, B. S., Longdon, J., et al. Biodiversity monitoring for a just planetary future. Science, 383(6678), 34-36, 2024.
Guha, N., Nyarko, J., Ho, D., Ré, C., Chilton, A., Chohlas- Wood, A., Peters, A., Waldon, B., Rockmore, D., Zam- brano, D., et al. LegalBench: A collaboratively built benchmark for measuring legal reasoning in large lan- guage models. Advances in Neural Information Process- ing Systems, 36, 2024.
Decardi-Nelson B. and You, F. Artificial intelligence can regulate light and climate systems to reduce energy use in plant factories and support sustainable food production. Nature Food, 5(10):869-81 2024.
Guo, T., Nan, B., Liang, 7.,, Guo, Z., Chawla, N., Wiest, O., Zhang, X., et al. What can large language models do in chemistry? a comprehensive benchmark on eight tasks. Advances in Neural Information Processing Systems, 36: 59662-59688, 2023.
Denis, P. and Baldridge. J. Joint determination of anaphoric- ity and coreference resolution using integer programming. In Human Language Technologies 2007: The Conference of the North American Chapter of the Association for
10

---

What Can Large Language Models Do for Sustainable Food?
Hartmann, C. and Siegrist, M. Consumer perception and behaviour regarding sustainable protein consumption: A systematic review. Trends in Food Science & Technology, 61:11-25, 2017.
Luo, Y., Gligorié, K., and Jurafsky, D. Othering and low status framing of immigrant cuisines in restaurant re- views and large language models. In Proceedings of the International AAAI Conference on Web and Social Media, volume 18, pp. 985-998, 2024.
Hopwood, C. J., Bleidorn, W. Schwaba, T., and Chen, S. Health, environmental, and animal rights motives for vegetarian eating. PloS One, 15(4)ee0230009 2020.
Madadkhani, S., Ramos, O. M., Chapman, M., Dunietz, J., Ouaknine, A., Rolnick, D., and Bengio, Y. Tackling climate change with machine learning: fostering the ma- turity of ML applications for climate change. In ICLR 2024 Workshops, 2024.
Horton, J. J. Large language models as simulated economic agents: What can we learn from homo silicus? Technical report, National Bureau of Economic Research, 2023.
Mathur, M. B. Ethical drawbacks of sustainable meat choices. Science, 375(6587):1362- 2022.
Huang, T., Lee, D., Sweeney, J., Shi, J., Steliotes, E., Lange, M., May, J., and Chen, M. Foodpuzzle: Developing large language model agents as flavor scientists. arXiv preprint DrXiv:2409.12832 2024.
Mattes, R. D. The taste for salt in humans. The American
journal of Clinical Nutrition, 65(2):692S-6975 1997.
Menus of Change. About Menus of Change menu- sofchange.org. https:/ / /www. www.moeecaanne.....o org/ about moc, 2024. [Accessed 29-04-2024).
Intergovermeental Panel on Climate Change. Climate change and land: an IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems. Technical report, 2019.
Morio, G. and Manning, C. D. An NLP benchmark dataset for assessing corporate climate policy engage- ment. In Thirty-seventh Conference on Neural Informa- tion Processing Systems Datasets and Benchmarks Track, 2023. URL https://p//// ctttts::/openrev//// /openreviwwwee..eooooee net/ forum? id-GF510F19Bt
Jiang, C., Shu, X., Qian, H., Lu, X., Zhou, J., Zhou, A., and Yu, Y. Limopt: Learning to define and solve gen- eral optimization problems from scratch. In The Twelfth International Conference on Learning Representations, 2025. URL https: opw.reveeeeeeeeeeeeeeeeee wwt. forum? id- 1d=9OMytboTJg.
Mylan, J., Andrews, J., and Maye, D. The big business of sustainable food production and consumption: Exploring the transition to alternative proteins. Proceedings of the National Academy of Sciences, 120(47):e2207778212) 2023.
Kaack, L. H., Donti, P. L., Strubell, E., Kamiya, G., Creutzig, F., and Rolnick, D. Aligning artificial intel- ligence with climate change mitigation. Nature Climate Change, 12(6):518 0000571777711.1 2022.
Pan, L., Albalak, A., Wang, X., and Wang. W. Logic- LM: Empowering large language models with sym- bolic solvers for faithful logical reasoning. Bouamor, H., Pino, J., and Bali, K. (eds.), Find- ings of the Association for Computational Linguis- tics: EMNLP 2023, pp. 3806-3824, Singapore, De- cember 2023. Association for Computational Lin- guistics. doi: 10.18653/v1/200 11..6633/12222..fft 12ndi.. findings-emnnn 248. URL https: aallannhhloggg org/2023. E Findings-emnip... 248/.
Kerth, C. R. The science of meat quality. John Wiley & Sons, 2013.
In
Lee, B. K., Mayhew, E. J., Sanchez-Lengeling. B., Wei, J. N., Qian, W. W., Little. K. A., Andres, M., Nguyen, B. B., Moloy, T., Yasonik, J., et al. A principal odor map unifies diverse tasks in olfactory perception. Science, 381 (6661): (6661):999-1006, 2023.
Li, B., Jiang, Y., Gadepally, V., and Tiwari, D. Sprout: Green generative Al with carbon-efficient LLM inference. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pp. 21799- 21813, 2024.
Park, S., Popowski, L., Cai, C., Morris, M. R., Liang, P., and Bernstein, M. S. Social simulacra: Creating popu- lated prototypes for social computing systems. In Pro- ceedings of the 35th Annual ACM Symposium User Interface Software and Technology, pp. 1-18, 2022.
Lohmann, P. M., Gsottbauer, E., Farrington, J., Human, S., and Reisch, L. A. Choice architecture promotes sustain- able choices in online food-delivery apps. PNAS Nexus, 3( :1a))2ga 2024.
Parkin, B. and Attwood, S. Menu design approaches to promote sustainable vegetarian food choices when dining out. Journal of Environmental Psychology, 79:101721, 2022.

---

What Can Large Language Models Do for Sustainable Food?
Patterson, D., Gonzalez, J., Le, Q., Liang, C., Munguia, L.- M., Rothchild, D., So, D., Texier, M., and Dean, Car- bon emissions and large neural network training, arXiv preprint arXXi:22/04. 10350, 2021.
20of 820p Samppaamn ant 2erobaased200maaae 2019. [Ac- cessed 03-01-2025].
Todhunter, M. E., Jubair, S., Verma, R., Saqe, R., Shen, K., and Duffy, Artificial intelligence and machine learning applications for cultured meat. Frontiers in Artificial Intelligence, 7:1 721422,,, 2024.
Poore, and Nemecek, T. Reducing food's environmental impacts through producers and consumers. Science, 360 (6392):987-992, 2018.
Tomsov, A. The Role Of Sensory Panels At Rede- fine Meat. https:/ /wwps rede f nemeat com/ blog/ wwt.ssennsorrsssse....e 2023. [Accessed 04-01- 2025].
Rateliff, W., Metzener, D. E.,, al. Pattern matching: the gestalt approach. Dobb's 's Journal, 13(7):46, 1988.
Roohani, Y., Lee, A., Huang, Q. Vora, J., Steinhart, Z., Huang, K., Marson, A., Liang, P., and Leskovec, An agent for design- ing genetic perturbation experiments. arXiv preprint arXiv:2405.17631, 2024.
Tuia, D., Kellenberger, B., Beery, S., Costelloe, B. R., Zuffi, S., Risse, B., Mathis, A., Mathis, M. W., Van Langevelde, F., Burghardt, T., et al. Perspectives in machine learning for wildlife conservation. Nature communications, 13(1):
BioDiscoveryAgent
1-15, 2022.
Saerens, W., Smetana, S., Van Campenhout, L.., Lammers, V., and Heinz, V. Life cycle assessment of burger patties produced with extruded meat substitutes. Journal of Cleaner Production, 306: 127177, 2021.
Van Wynsberghe, A. Sustainable ai: Ai for sustainability and the sustainability of ai. Al and Ethics, 133):213-218, 2021.
Santurkar, S., Durmus, E., Ladhak, F., Lee, C., Liang, P., and Hashimoto, T. Whose opinions do language mod- els reflect? In International Conference on Machine Learning, pp. 19971430004. PMLR, 2023.
VanderWeele, T. J. and Mathur, M. B. Some desirable properties of the bonferroni correction: is the bonferroni correction really so bad? American Journal of Epidemi- ology, 188(3):617-611 2019.
Scherer, L., Behrens, P., and Tukker, A. Opportunity for di- etary win-win-win nutrition, environment, and animal welfare. One Earth, 13))349-360, 2019.
Varela, P. and Ares, G. Sensory profiling, the blurred line between sensory and consumer science. a review novel methods for product characterization. Food Research International, 48(:):893 908, 2012.
Si, C., Yang, D., and Hashimoto, T. Can LLMs generate novel research ideas? A large-scale human study with 100+ NI.P researchers. arXiv preprint arXiv:2409.04100 2024.
Waschulin, V. and Specht, L. Cellular agriculture: An exten- sion of common production methods for food. https:
//gfi. otgl wwwpe/etoooettnnooeeeeeeeeeteeee //geg//..orrroooocccccc/////aaaa Gelluaar-ggrcccutte-------aaaaa- /221///////cdd///222///////
Spence, C. Just how much of what we taste derives from the sense of smell? Flavour, 4:1-10, 2015.
pdf/, 2018. [Accessed 06-01-2025).
Weijers, R. J., Claessens, I. W., Gillebaart, M., and de Rid- der, D. T. Nudging towards sustainable dining: Explor- ing menu nudges to promote vegetarian meal choices in restaurants. Appetite, 198:107376, 2024.
St. Pierre, S. R., Darwin, E. C., Adil, D., Aviles, M. C., Date, A., Dunne, R. A., Lall, Y., Parra Vallecillo, M., Perez Medina, V. A., Linka, K., et al. The mechanical and sensory signature of plant-based and animal meat. upj Science of Food, 8(1):94, 2024.
Wolsey, L. A. Integer programming. John Wiley & Sons, 2020.
Swanson, K., Wu, W., Bulaong, N. L., Pak, J. E.,, and Zou, J. The virtual lab: Al agents design SARS-CoVa nanobodies with experimental validation. bioRxiv, pp. 2024-11, 2024.
Xu, X., Sharma, P., Shu, S., Lin, T.-S., Ciais, P., Tubiello, F. N., Smith, P., Campbell, N., and Jain, A. K. Global greenhouse gas emissions from animal-based foods are twice those of plant-based foods. Nature Food, 2(9): 724-732, 2021.
The New York Times. Fake Meat vs. Real Meat.
03/ 03// -eaat.
Yang, C., Wang, X., Lu, Y., Liu, H., Le, Q V., Zhou, D., and Chen, X. Large language models as optimizers. In The Twelfth International Conference on Learning Represen- tations, 2024. URL https: oppnreview. net// forum d=bb4VOOWEL..
Real$
20Meat, 8 https:/000ssstttt:atorpoeeeeeee 20initccioos3222200900tttteeece1 20mea Beware
12

---

What Can Large Language Models Do for Sustainable Food?
Ye, X., Chen, Q. Dillig, I., and Durrett, G. SatLM: Satisfiabilit-aiidedd language models using declarative prompting, Advances in Neural Information Processing Systems, 36, 2024.
Zhang, E., Wang. X., Gong, P., Lin, Y., and Mao, J. USimA- gent: Large language models for simulating search users. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2687-2692 2024.
13

---

What Can Large Language Models Do for Sustainable Food?
A. Proof of Proposition 6.1
The problem we would like to solve is:
N
[(x) = Σ p( + Ad(x)
max
2m1
N
(3)
Σ K i=1
2, E (0,1) ViE Vi [I.
However, we do not have access the true scores p(u:), e.g. actual satisfaction of a specified population with recipe.
Instead, we use LLM to produce the estimates p(u,), and solve:
max f(x) = Σ p(u))=; + Ad(x)
N Σ 24 K
(4)
#4 E (0. 1) E [I....,N]
Let x² be the optimal solution of (3), and let a* be the optimal solution of (4). Proposition A.1. Let p(u:), E [0, 1]. and d(xx) E [0, D). If f(u,())) p(u:)] S e all u, U, then [f( f(=") fx=')|
s
2Ke.
Proof. First, note that Vz, [x( - f(x) < Kc. Then, combining this with the optimality of xt and 8", f(i²)
f(é*) - Ke 2 f(x²) - Ke 2 fxx') 2KE.
B. Extended Related Work
Large Language Models and Reasoning. Our work broadly pertains to the integration of LLMs with other tools for reasoning tasks. Ahmaditeshnizi et al. (2024) develop an agent called OptiMUS for formulating and solving (mixed integer) linear programming problems from their natural language descriptions. Their goal is to expand access to solvers to individuals who lack the expertise required to formulate and solve these problems. Jiang et al. (2025) propose five- element formulation for parameterizing optimization problems and fine-tune LLMs for generating five-element formulations and solver code from natural language descriptions of optimization problems. Ye et al. (2024) propose SatLM, which combines LLMs with SAT solvers solve problems in arithmetic reasoning, logical reasoning, and symbolic reasoning. Pan et al. (2023) propose Logic-LM, which integrates LLMs with symbolic solvers improve logical reasoning. Relative to these works, our setting is fundamentally different, where the underlying optimization problem is not fully specified due to involving subjective human preferences or other components that are difficult to formalize mathematically. We are specifically motivated by the food domain, in which human satisfaction must considered (alongside other factors such as nutrition, cost, emissions, etc.), but difficult to write down mathematically. Similar problems arise in settings such as education (e.g. curriculum design) and health (e.g. fitness regimen design). Our framework is also applicable settings where it may take considerable effort to formally specify the optimization problem, e.g. computing climate impacts as well as cost, nutrition, etc. for each food item, but an LLM may be able to generate sufficiently accurate estimates. We also note past work in the natural language processing literature on combining integer programming with machine learning, e.g. Denis & Baldridge (2007), which used integer linear programming for coference resolution.
Large Language Models for Climate, Sustainability, and Food. Bulian et al. (2024) propose an evaluation framework for LLMs in the domain of climate information. Morio & Manning (2023) create a benchmark dataset for assessing corporate climate policy engagement and evaluate several language models. Huang et al. (2024) introduce the FoodPuzzle dataset and create an I.LM-bast based agent for flavor profile prediction. They define two tasks: molecular food prediction, in which
14

---

What Can Large Language Models Do for Sustainable Food?
the goal is to predict food sources based on their molecular composition, and molecular profile completion, in which the goal is to identify the missing molecules needed to complete the molecular profile of given food item. Within our paper, the most closely related task is sensory profile prediction. Our work differs in that we evaluate LLMs on their ability to predict dimensions otherwise evaluated by a human sensory panel, such as overall satisfaction, purchase intent, meatiness, etc. prevalent in the current practice of product development in food science. Sensory panels are essential but also very expensive and time consuming to both run and analyze. Additionally, our evaluation specifically focuses on sustainable protein rather than general food science. Finally, Huang et al. (2024) develop an agent based on in-context learning and retrieval augmented generation, whereas we evaluate LLMs on our sustainable protein tasks via zero-shot prompting, as well as integration with combinatorial optimization.
Data-Driven Optimization Our work also pertains to the literature on data-driven optimization. Previous work has proposed the "predict-then-opimizz" framework in which a predictive model is used to define the parameters of an optimization problem (Elmachtoub & Grigas, 2022; Bertsimas & Kallus, 2020). We extend the predict-then-optimie framework in the setting of combinatorial optimization by incorporating a component where the elements of the ground set (e.g. recipes) are generated, and applying LLMSs both the generation and prediction steps.
Artifical Intelligence for Climate, Sustainability, and Food. Previous work has studied climate- and environment- related applications of artificial intelligence (Tuia et al., 2022; Chapman et al., 2024; Madadkhani et al., 2024; Kaack et al., 2022). Within the food domain, previous work has explored how Al can reduce energy use plant factories to support sustainable food production (Decardi-Nelsop & You, 2024). St. Pierre et al. (2024) demonstrated that a combination of mechanical testing and ML can describe food texture in similar manner human taste testers.
C. Supplementary Information on Datasets
C.1. NECTAR Dataset
The NECTAR dataset was collected over the period of June to August 2023 in Precision Research's Chicago research center. It is only available to academic researchers to reduce harm to the reputations of companies whose products performed poorly in the taste testing. Additional information can be found at https: / / /www. nectar org/taste. It consists of 5,516 sensory evaluations of 47 products, and will continue to grow in size over the coming years. Each sensory evaluation consists of 21 dimensions, shown in Table 3. The products were prepared in test kitchen according to manufacturers' instructions. Plant-based product selection criteria were based on popularity, availability (i.e. distributed in markekeet the time of the test), and similarity to analog animal offerings (i.e. veggie burgers made from whole plants were not included, whereas plant-based burgers aiming to mimic the eating experience of animal-based burgers were included), All tasting was blind and monadic (one product at a time), and the panel was untrained.
C.2. Food. com Recipe Dataset
Figure C.2 shows an example recipe from the F Food. com dataset. We also show some example pairs from this dataset.
D. Supplementary Methods
D.1. Experimental Design
The 30 sustainable protein products used in this task consisted of six products from each of the five categories. Further
information on the food scientists is in Table 6. The prompt for Phase is shown in Figure 7, and our standardization prompt is shown in Figure 8. Precise definitions of the dimensions of evaluation in Phase 2 are in Section 1.2. In Phase 1, 15 food scientists participated, each evaluating 2 products. In Phase 2, again 15 food scientists participated, each evaluating 2 products (with one human and one LLM response per product), with an overlap of 10 food scientists between Phase and Phase 2.
15

---

What Can Large Language Models Do for Sustainable Food?
Sensory Dimension
Question Type
Burger
Hot Dog
Bacon
Chicken Nuggets
Chicken Tenders
Appearance
pt Likert Scale
X
X
X
X
Color
7 pt Semantic Differential
X
X
X
X
X
Flavor Liking
pt Likert Scale
X
X
X
X
X
Spiciness
7 pt Semantic Differential
X
X
X
X
X
Like
Open Ended
X
X
X
X
X
Dislike
Open Ended
X
X
X
X
AAXAK X
Overall Liking
pt Likert Scale
X
X
X
Meatiness
7 pt Semantic Differential
X
X
X
Greasiness
pt Likert Scale 7 pt Semantic differential
X
X
X
Juiciness
X
X
X
X
Smokiness
7 pt Semantic Differential
X X
Sweetness
7 pt Semantic Differential
Saltiness
7 pt Semantic Differential
X
X
X
X
Crispiness
7 pt Semantic Differential
X
X X
Aftertaste Strength
pt Likert Scale pt Semantic Differential
X
Aftertaste
X
X
X
Purchase Intent
5 pt Likert Scale
X
X
X
Texture: Liking
7 Likert Scale 7 Semantic Differential
X
X
Chewiness
X
X
X
X
Firmness
7 pt Semantic Differential
X
X
X
X
X
Breading Flavor
7 pt Semantic Differential
X
Table 3. Overview of sensory dimensions NECTAR dataset, by category of sustainable protein product.
Task
Data
Techniques Used
Evaluation Metrics
Sustainable protein: ex- perimental design
NECTAR
Zero-shot prompting
Accuracy, Specificity, Comple- mentarity, Time Saved (Expert Hu- man Food Scientist Evaluation)
Traditional foods: menu design
Menu from Banerjee et al.
Zero-shot prompting, inte- quadratic programming
Satisfaction (Human Subjects Evaluation). Emissions, Animal Welfare (Automated)
(2023), emissions data from
Poore & Nemecek (2018),
animal welfare data from Faunalytics (2020)
Sustainable protein: sen- sory profile prediction
NECTAR
Zero-shot prompting
Accuracy (Automated)
Traditional foods: recipe preference prediction
Food.com
Zero-shot prompting
Accuracy (Automated)
Table 4. Overview of tasks, techniques used, and evaluation metrics.
Original
Edited
o1 -prreviww
319.8 (37.7)
327.6 (87.2)
Food Scientist
311.3 (223.9)
311.6 (195.3)
Table Original and edited lengths ol-preview's food scientists' experimental designs. Standard deviation is in parentheses.
I. PROPERTIES OF EXPERT FOOD SCIENTISTS
20 food science experts were recruited via direct outreach on Linkedln or email from our food scientist team member. Their properties are shown in Table
16

---

What Can Large Language Models Do for Sustainable Food?
Example Recipe
Name:
Carina's Tofu- Vegetable Kebabs
Description: This dish is best prepared day advance to allow the ingredients to soak in the marinade overnight. Ingredients: extra firm tofu, eggplant, zucchini, mushrooms, soy sauce, low sodium soy sauce, olive oil, maple syrup, honey, red wine vinegar, lemon juice, garlic cloves, mustard powder, black pepper. Instructions:
1. Drain the tofu, carefully squeezing out excess water, and pat dry with paper towels.
2. Cut tofu into one-inch squares.
3. Set aside. Cut eggplant lengthwise half, then cut each half into approximately three strips.
Cut strips crosswise into one-inch cubes. Slice zucchini into half-inch thick slices. Cut red pepper in half, removing stem and seeds, and cut each half into one-inch squares. Wipe mushrooms clean with moist paper towel and remove stems.
8. Thread tofu and vegetables to barbecue skewers in alternating color combinations: For example, first piece of eggplant, then slice of tofu, then zucchini, then red pepper, baby corn and mushrooms.
9. Continue in this way until all skewers are full.
10. Make the marinade by putting all ingredients in blender, and blend on high speed for about one minute until mixed.
Alternatively, put all ingredients in glass jar, cover tightly with the lid and shake well until mixed.
12. Lay the kebabs in long, shallow baking pan on non-metal tray, making sure they lie flat. Evenly pour the marinade over the kebabs, turning them once that the tofu and vegetables are coated.
13. Refrigerate the kebabs for three to eight hours, occasionally spooning the marinade over them.
14. Broil or grill the kebabs at 450 for 15-20 minutes, or on the grill, until the vegetables are browned.
15. Suggestions This meal can be served over cooked, brown rice. Amounts can easily be doubled to make four servings.
Figure 3. Example recipe from the Food. dataset.
Example Pair 1: Recipe
Corned Beef Dinner Crock Pot
Ingredients: corned beef brisket, onion, celery, potatoes, carrots, bay leaf, garlic clove, Worcestershire sauce, dry mustard, cabbage, caraway seed.
Instructions: Trim brisket of all visible fat cut to fit 4 qt larger crockpot if necessary. Place onion celery potatoes and carrots in bottom of crockpot lay brisket on top. Whisk together bouillon bay leaf garlic Worcestershire sauce and dry mustard. Pour over brisket cover pot. Cook on low setting for to 10 hours adding cabbage wedges and caraway seed for the last hour of cooking. To serve discard cooking liquid slice meat onto hot serving plates accompany with the cooked potatoes earrots celery cabbage wedges and your favourite mustard.
Example Pair 1:
Recipe 2
Crock Pot Corned Beef and Cabbage
Ingredients: corned beef brisket, onions, cabbage, pepper, vinegar, sugar, water. Instructions: Combine ingredients in crock pot with cabbage on top. Cut meat to fit if necessary. Cover and cook on low 10-12 hours; high 6-7 hours or auto 6-8 hours.
Figure 4, Example pair from the Food, com dataset.
D.1.2. EVALUATION DIMENSIONS
The dimensions of evaluation were defined to the food scientists as follows:
17

---

What Can Large Language Models Do for Sustainable Food?
Example Pair 2: Recipe
Oven-Fried Chicken Chimichangas
salsa, ground cumin, dried oregano leaves, cheddar cheese, green onions, flour tortillas, margarine, cheddar cheese, green onion.
Ingredients:
Instructions: Mix chicken picante sauce salsa cumin oregano cheese and onions. Place about 1/4 cup of the chicken mixture in the center of each tortilla. Fold opposite sides over filling. Roll up from bottom and place seam-side down on baking sheet. Brush with melted margarine. Bake at 400 for 25 minutes or until golden. Garnish with additional cheese and green onion and serve salsa on the side.
Example Pair 2: Recipe 2
Chicken Rice Casserole
Ingredients: butter, flour, dried thyme, chicken broth, milk, salt, black pepper, cooked rice, parmesan cheese.
Instructions: Melt 3 tablespoons of butter in saucepan over medium heat whisk in flour and thyme and cook for 1 minute. Gradually stir in broth and milk stirring until thick and smooth. Stir in chicken and add salt and pepper, set aside. Grease a 1/2 quart shallow baking dish. Spread rice prepared baking dish sprinkle with the peas and then pour creamed chicken mixture over. Dot with remaining tablespoon butter and sprinkle with bread crumbs and cheese which you mix together. Bake 400 until hot and bubbly about 20 25 minutes.
Figure S. Example pair from the F Food. com dataset.
Example Pair 2: Recipe
Bow Tie Pasta With Broccoli and Broccoli Sauce
Ingredients: broccoli, water, bow tie pasta, butter, salt.
Instructions: Cut the broccoli buds into small florets leaving as short a stem as possible; aside; cut the remaining broccoli stems into 1/2" pieces; boil them covered the 1/4 cups water lightly salted for 15 minutes or until very soft. Meanwhile boil the pasta in salted water until al dente; drain reserving 1/2 cup of the cooking water; return the pasta to the pot and stir until all moisture is evaporated. Place the reserved florets into small pot and steam boil minutes retaining the bright green color and bit of crunch. In a food processor puree the cooked broccoli and its cooking water until very smooth adding a bit of the reserved pasta water needed to make a smooth sauce; add the butter and pulse until melted; add salt to taste. Pour warm sauce over the pasta and stir gently to combine; plate and sprinkle with drained florets.
Example Pair 2: Recipe 2
Lemon Garlic Pasta
Ingredients: garlic, extra virgin olive oil, lemon juice, chicken flavor instant bouillon, pepper, spaghetti, angel hair pasta, parmesan cheese, parsley.
Instructions: In large skillet cook garlic in olive oil until golden. Add lemon juice bouillon and pepper. Cook and stir until bouillon dissolves. large bowl toss pasta garlic mixture cheese and parsley; serve immediately.
Figure 6, Example pair from the Food, com dataset.
Accuracy: Are these suggestions likely to be useful in improving the product, to the best of your knowledge? Are they consistent with the provided feedback?
Specificity: How detailed and actionable are the suggestions?
Complementarity: Did this scientist see anything you missed? Would they be complementary as a brainstorming partner?
18

---

What Can Large Language Models Do for Sustainable Food?
Prompt
You are an expert plant-based meat food scientist. You have devised a <ccategory>> product with the following ingredient list: <ingredient list>.
Additionally, it has the following nutritional information: <nutrition faetss
You blind taste test of American omnivores and received the following quantitative feedback on your product:
<quantitative feedback>.
Additionally, you received the following qualitative feedback about what people liked:
<positive feedback>>
You also received the following qualitative feedback about what people disliked: <negative feedback>>
What changes would you consider making to your product? Could you design a set experiments on the key areas that need improvement? You will be evaluated on metrics including aceuracy and specificity.
Figure 7. Prompt for Phase of the experimental design task, in which ol ol-preview and expert food scientists generated experimental designs for improving sustainable protein products on the basis of sensory panel feedback. For the food scientists, the first line was removed. ol -p-prewe was additionally instructed to limit its responses 250 words, to approximately match the mean length of the food scientists' responses. Even though the mean original human response length was 311 words (Table 5), 250 was chosen in the instruction ol ol-peeieew because noticed that ol opeevveww tended exceed the word limit.
Prompt
You are a writing assistant specializing in editing writing produced by food scientists. will give you some text to edit.
Instructions:
Convert all suggestions to a numbered list, with a title for each suggestion. Do not include any content that not part of the numbered list.
2. Do not change the length.
3. Remove any references to the author's personal experience or to other writing.
4. Rewrite it as if could have come from either human or LLM.
5. Use complete sentences.
Do not add any prefix like 'Here the edited text'. Just output the edited text. Do not add or remove any the meaning, unless necessary for following instruction #3.
Do not use asterisks.
Here is the text:
<original text>
Figure Prompt for style standardization step of the experimental design task.
Phase (n = 15)
Phase (n = 15)
All (n = 20)
Mean Years of Experience in Plant-Based Food Science
4.33 (2.78)
4.73 (5.30)
5.05 (4.73)
Mean Years of Experience Plant-Based Meat
2.23 (2.01)
3.27 (3.82)
3.08 (3.38)
Table Properties of food scientists. Standard deviation in parentheses. 10 food scientists participated in both Phase and Phase 2, but we ensured that they did not evaluate their own experimental designs.
Time saved: How much time, if any, do you think collaborating with this scientist would save you in designing full (detailed and implementable) experimental design plan to improve this product? Please specify in hours.
D.2. Menu Design
The original menu we use is from Banerjee et al. (2023), who chose the items from Deliveroo's and Just Eat's top 100 items ordered in the United Kingdom in 2019. They also adjusted the items following pilot surveys. The menu includes 19 non non-eegeta and 17 vegetarian or vegan items. The original paper says 18 non-vegetarian and vegetarian vegan
19

---

What Can Large Language Models Do for Sustainable Food?
options, but a dish called "Panchetta Carbonara" was included in the vegetarian section. For some items, the original menu included a "(v)" parenthetical indicating that vegan option is available. We removed these parentheticals for simplicity in survey design and computing GHG emissions. Following Banerjee et al. (2023), GHG emissions were estimated based on the main ingredient of the dish. Data from Poore & Nemecek (2018) were used to compute emissions. For animal welfare, data from Faunalytics, a leading animal welfare organization, on number of animals killed per kilogram of various meats were used (Faunalytics, 2020). Two meats in the original menu, lamb and duck, were not contained in the Faunalytics data, and instead were imputed using the value for turkey, an animal of roughly similar size. Table shows the sample sizes per arm the Prolific experiments.
Arm
Sample Size
Original
50
Vegetarian Subset
50
Remove Beef
Vegetarian First
-pepvivwww
49
ol ol-preview+IQP
50
ol-preview RD
50
GPT-40+IQP
50
Llama+IQP
52
Claude+IQP
50
Gemini+IQP
Table 7. Sample sizes per arm in Prolific randomized experiments for the menu design task. "ol-preview RD" is an ablation in which ol ol-preview's 's descriptions were replaced with a simple list of ingredients.
Other than the choice of dish, the participants were also asked the following questions:
How satisfied are you with your set of choices? 1: not at all. 10: very satisfied.
How likely would you be visit this restaurant, assuming is affordable and a convenient distance from you? I: not likely at all. 10: very likely.
How likely would you be recommend this restaurant to your friends? 1: not likely at all. 10: very likely.
D.3. Sensory Profile Prediction
Figure 12 shows the prompt for the sensory profile prediction task. Table 8 shows the number of pairs for each sensory dimension. Table 9 shows sample sizes across all prediction tasks.
N
Overall Satisfaction
85
Meatiness
61
Greasiness
103
Juiciness
73
Sweetness
28
Saltiness
63
Purchase
82
All Dimensions
495
Table 8. Sample sizes (number of pairs) for each sensory dimension in the sensory profile prediction task.
D.4. Recipe Preference Prediction
Figure 13 shows the prompt for the recipe preference prediction task.
20

---

What Can Large Language Models Do for Sustainable Food?
Prompt
You are a brilliant chef experienced at creating sustainable and delicious food.
Here a menu: <original menu>
Please generate revised menu, with the same number recipes (<n>) and no new ingredients other than tofu, lentils, mushrooms, chickpeas, eggs, and cheese.
Design the menu achieve at least 75% CO2 emissions reduction in people's choices while maintaining improving patron satisfaction with their set of choices.
Patrons will be American omnivores. Emissions will be computed based on the main (first) ingredient. Please output each recipe in same format as this example:
Tofu curry ramen
Fried tafus, noodles, curry broth, pak choi, pickled onions. Appealing description.
The ingredients must be in order of usage, i.e the main ingredient must come first. Very important: you must only use ingredients in the original menu or the list above. For every ingredient, there must be an exact match in the original menu or the list above. Do not worsen cost, nutrition, animal welfare (number of animals used, computed based on the first ingredient), or preparation time.
not include any stars, asterisks, hashtags, underscores. Do not number the recipes. Do not include any text other than recipe information, e.g. do not say 'Here are the recipes'.
Figure 9. LLM prompt used in the menu design ablation, in which ol -preview on its own was asked to directly revise the original menu.
Prompt
You are brilliant chef experienced at creating sustainable and delicious food. Here is menu: <original menu>> Please generate (<k>> new, delicious, and diverse vegan or vegetarian dishes from this set of ingredients. You are also allowed to use tofu, lentils, mushrooms, chickpeas, eggs, and cheese. Patrons will be American omnivores.
Please output in same format as this example:
Tofu curry ramen
Fried tofu, noodles, curry broth, pak choi, pickled onions. Appealing description.
The ingredients must be in order of usage, i.c the main ingredient must come first.
Very important: you must only use ingredients in the original menu or the list above. For every ingredient, there must be an exact match in the original menu or the list above.
not worsen CO2 emissions, cost, nutrition, or preparation time. Emissions will be computed based on the main (first) ingredient.
Do not include any stars, asterisks, hashtags, underscores. Do not number the recipes. Do not include any text other than recipe information, e.g. do not say 'Here are the recipes'.
Figure LLM prompt used the recipe generation stage of the LLM+IQP approach.
E. Supplementary Results
E.1. Experimental Design
Figures 14 and 15 show example output from ol ol-preveew.
E.2. Menu Design
Table 11 shows accuracy of the predicted preferences.
21

---

What Can Large Language Models Do for Sustainable Food?
Prompt
Here are <>> recipes. Please rate them on a scale of 1-10 based on standard American omnivore taste preferences, I being unappealing and being appealing. Output only a comma-separated list of <>> numbers, from to 10. <recipes>
Figure 11. LLM prompt used in the rating stage of the LLM+1QP approach.
Prompt
You are an expert plant-based meat food scientist. Here are the ingredient lists of two <calegory>> products. Product 1: <ingredient list>
Product <ingredient list>
Additionally, here are the nutrition facts for product I: <nutrition facts>.
And here are the nutrition facts for product 2: <nutrition facts>.
Now, suppose that group of 100 omnivores eats both products in blind taste test. Which do you predict would be ranked higher on the dimension of <dimension>? Please output a single character, either 1 or 2 on the first line.
Figure 12, LLM prompt for the sensory profile prediction task.
Claude 3.5 Sonnet
Gemini 1.5
GPT-3.5 Turbo
GPT-4o
Llama
ol-preview
Baseline
Sensory Profile Prediction: LLM Prediction Sensory Panel (Sustainable Protein)
All Dimensions
495
495
495
495
495
495
495
Overall Satisfaction
85
85
85
85
85
85
85
Meatiness
61
61
61
Greasiness
103
103
103
103
103
103
103
Juiciness
73
73
73
73
73
73
73
Sweetness
28
28
28
28
28
28
28
Saltiness
63
63
63
63
63
63
Purchase
82
82
82
82
82
Recipe Preference Prediction (Food. com):
LLM Prediction vs. Rating (Traditional Foods)
Rating
500
500
500
500
500
500
500
Recipe Preference Prediction (Menu Design):
LLM Prediction Order Frequency (Traditional Foods)
Order Frequency
1225
1275
1225
1326
1225
1225
Table 9, Sample sizes (number of pairs) for prediction tasks.
E.3. Sensory Profile Prediction
The performance an expert food scientist specifically for the 'Oerrall Satisfaction' dimension shown Table 14. Accuracies each quartile of the ground truth preference gap are shown in Tables 15, 16, 17, 18.
E.4. Recipe Preference Prediction
Tables 19 and 20 list supplementary analyses of performance on the recipe preference task. Table 21 shows performance on smaller held-out set.
22

---

What Can Large Language Models Do for Sustainable Food?
Prompt
You expert online recipe writer. Which online recipe would people prefer, recipe recipe 2? Output a number only (1 2). You must choose one. If unsure, provide your best guess.
Recipe 1: <recipe text>>
Recipe 2: <recipe text>>
Answer:
Figure 13. LLM prompt the recipe preference prediction task.
Example Output
Appearance Enhancement
Many participants found the appearance unappetizing, describing it lsoko like dog trewt,""raww" "fake."
Experiment: Test natural coloring agents achieve authentic bacon look. Incorporate beet juice paprika extract to enhance redness and use coconut oil for marbling effects.
Variables: Vary concentrations of coloring agents and adjust cooking times and temperatures to achieve desired caramelization.
Metrics: Conduct sensory evaluations focusing on appearance, measuring scores against the current mean 2.47.
Texture Improvement
Issues with mushy center and inconsistent crispiness were noted.
Experiment: Adjust the protein blend ratios and incorporate texturizers like methylcellulose to enhance firmness and coherence.
Variables: Test different ratios of soy and wheat proteins; vary levels of konjac powder and vegetable gums,
Metries: Use texture profile analysis to measure hardness and chewiness; collect sensory feedback on texture aiming to improve the mean from 3.43 towards (neutral).
Flavor Enhancement
Participants desired more smokiness and found the taste bland.
Experiment: Increase natural smoke flavoring and seasoning levels to enhance meatiness and smokiness. Variables: Experiment with different types and concentrations of smoke extracts (e.g., hickory, applewood) and adjust salt and sugar levels.
Metrics: Sensory evaluations on flavor and smokiness, targeting improvements from means of 3.58 and 2.43 towards
Reduce Greasiness
Some found the product too greasy.
Experiment: Reduce sunflower oil content replace it with less greasy alternative like canola oil.
Variables: Vary oil types and quantities.
Metrics: Measure perceived greasiness in sensory tests, aiming to adjust the mean from 2.53 to 3 (moderately greasy).
Figure 14. Example ol preview output for the experimental design task, after style standardization.
23

---

What Can Large Language Models Do for Sustainable Food?
Example Output
Improve Texture and Crispiness. Issue: The product is perceived as too soft and lacking crispiness (Mean crispiness rating: 5.86; ideal is 4).
Changes:
Adjust ingredient composition by incorporating crisping agents like rice flour or modifying the ratio of wheat
gluten reduce chewiness.
Modify the process by slicing the product thinner and adjusting cooking temperatures and times achieve crisper texture.
Experiment:
Design a factorial experiment testing different levels of rice flour (e.g., 1%, 2%, 3%) and cooking temperatures (e.g., 350 F75 F)0*F).
Measure the crispiness using texture analysis and conduct sensory evaluations.
Enhance Flavor Profile. Issue: Flavor is disliked, with complaints of excessive saltiness and artificial taste (Mean
flavor rating: 2.22; saltiness rating skewed towards too salty). Changes:
Reduce salt content by gradually decreasing salt levels by 10-20%.
Increase umami and smokiness by adding natural umami enhancers like mushroom extract or increasing natural
smoke flavors.
Balance sweetness by introducing a small amount of natural sweeteners like maple syrup to mimic bacon's sweet-salty balance. Experiment:
Create series of formulations with varying salt levels (e.g., full, -10%, -20% and added sweeteners. Conduct sensory tests focusing flavor liking and saltiness perception.
3. Improve Appearance. Issue: Appearance is inconsistent; some find it artificial (Mixed feedback color; Mean color rating: 4.42).
Changes:
Introduce marbling by using natural colorants to create a marbled fat appearance, mimicking real bacon.
Adjust color by modifying the hue using oleoresin paprika for red tones and annatto for lighter shades.
Experiment:
Develop prototypes with different marbling techniques and colorant levels.
Perform visual assessments with consumers to rate appearance authenticity.
4. Increase Greasiness. Issue: Product is not perceived as greasy enough (Mean greasiness rating: 1.39; ideal is 3).
Changes:
Incorporate plant-based fats by increasing the amount of oils like coconut or sunflower oil.
Use fat release technology by employing oil encapsulation that melts during cooking to simulate bacon fat. Experiment:
Test formulations with varying fat levels (e.g., 2%, 4%, 6% additional oil). Evaluate greasiness through sensory panels and measure perceived oiliness.
Figure 15. Example ol- ooepreeeww output for the experimental design task, after style standardization.
Test
Accuracy
Complementarity
Specificity
Percent Time Saved
Ł-test
0.121
0.350
0.003
0.0002
Paired t-test
0.125
0.368
0.003
0.0003
Wilcoxon signed-rank test
0.078
0.284
0.005
0.0007
Linear mixed model with random effect for evaluator ID
0.075
0.993
0.001
0.0002
Permutation test
0.140
0,392
0.003
0.0003
OLS
0.1812
0.416
0.001
0.0006
Table 10. Sensitivity of p-values in the experimental design task to choice of statistical test. The paired 1-test takes into account the pairing across products. The Wilcoxon signed-rank test is exact, for paired samples, nonparametrie, and does rely on normality assumptions. The permutation is also exact and does not rely on normality assumptions. In the OLS, we control for product ID, evaluator ID, generator ID fixed effects.
24

---

What Can Large Language Models Do for Sustainable Food?
LLM
Accuracy
Claude 3.5 Sonnet
0.85
Gemini 1.5 Pro GPT-3.5 Turbo GPT-4o
0.72
0.82
Llama 3.1 70b Instruct ol-preview ol-preview,
0.72
0.50
0.84
Table 1. Accuracy in the fourth (highest) quartile of the preference gap, for predicted preferences in the menu design task vs. actual order frequency. The 25th and 50th percentile of the preference gap were both (the minimum gap needed for inclusion in the analysis in Table 2). so we did not test those quartiles. Accuracies statistically significant according to chi-squared test are in bold. GPT-335 Turbo did not produce valid output this alone (not including the full description), which could explain the discrepancy between item- prediction performance and menu-level performance opppeeie When its descriptions were replaced with a simple ingredients (in one ablations), ol ol-preview' accuracy in this quartile was 84% (p=2.40e-20).
thus was not tested. We note that LLM predictions were based
recipe title
Outcome
Treatment Effect Estimate (p-value)
Percent Change
Emissions
34.31 (1.37e-18)
78.55
Satisfaction with Set of Choices
-0.04 (0.93)
-0,58
Likelihood of Visiting
-0.07 (0.89)
-0.95
Likelihood of Recommending to a Friend
-0.38 (0.47)
-4.9
Animal Usage
0.00 (0.91)
-3.5
Table 12. Treatment Effect Estimates (p-value in parentheses) and percent change for ol orppreview+IQP vs. the original menu, adjusting (via linear regression) for the covariates of age, gender, race, dietary preference. Signs are flipped so that positive values better all cases. Compared to the original menu, prevvvvie++IQP reduces average emissions by 79% while maintaining participants's 's satisfaction with their set of choices to within % of the original. Small reductions are observed in likelihood of visiting, likelihood recommending friend, and animal welfare, but are statistically significant.
Emissions
Satisfied
Visit
Recommend
Animals
PB
LLM Gen. (AO)
LLM Gen. (AC)
Original
44.91
8.40
7.90
7.82
0.08
0.20
0.00
0.00
Expert Chef
41.28
8.20
7.76
7.49
0.08
0.27
0.00
0.00
Vegetarian Subset
19.87
6.82
6.76
6.36
0.00
1.00
0.00
0.00
Remove Beef
21.65
7.74
7.50
7.08
0.16
0.24
0.00
0,00
Beef to Chicken
17.57
8.04
8.00
7.98
0.15
0.36
0.00
0.00
Vegetarian First ol or-previe-+IQP
37.37
8.20
7.74
7.62
0.13
0.28
0.00
0.00
8.70
8.44
7.80
7.46
0.54
0.50
0.44
ol ol-preview
9.64
6.51
6.29
6.22
0.00
1.00
1.00
1.00
ol -repreview+IQP. RD
11.05
7.48
6,98
6.76
0.18
0.36
0.50
0,20
ol IIQP RP
9.44
7.84
7.63
7.33
0.12
0.55
0.36
0.39
ol ---prviee==QPP RDi
9.75
8.16
7.96
7.66
0.50
0.47
0.34
ol p-reviiew=-QPP RPDi
8.54
7.12
6.56
6.50
0.07
0.54
0.47
0.42
Claude+IQP
10.16
7.30
6.68
6.86
0.16
0.38
0.42
0.28
Gemini+IQP
11.71
7.59
7.35
7.02
0.19
0.31
0.44
0.10
GPT-4++IQP
8.53
7.38
7.08
6.98
0.17
0.52
0.42
0.36
Llama+IQP
10.68
7.77
7.54
7.21
0.12
0.58
0.50
0.31
Table 13. Table of all results for the menu design task. "Emissions" is in kg COzeq per kg food item. "Animal Lives" is number of animals killed per kg of food item. "Satisfied", "Visit", and "Recommend" are on a ten point (1 to 10) scale. "PB" is the fraction of choices that were plant based (either vegan or vegetarian). "LLM Gen. (AOY" is fraction of available options the final optimized menu that were LLM generated (as opposed to recipes from the original menu). "LLM Gen. (AC)" is the fraction of actual choices that were LLM generated (as opposed to recipes from the original menu). -IP RD" al -previiss''s descriptions are replaced with a simple list of ingredients. In "ol-preview+IQP. RP", the preferences component of the objective (the first term in Problem 2) is removed. In "ol- previcw+IQP, RDi", the diversity component of the objective is removed, i.c. 0 Problem 2. In "ol- -reprvview+IQR. RPDi", both the preference and diversity components of the objective in Problem 2 are removed, i.c. becomes feasibility problem.
25

---

What Can Large Language Models Do for Sustainable Food?
Emissions
50
Emissions COyeq kg of food
30
20
10
Original
Expent Chaf
Vegetarian Subset
Remove Beat
Beef to Chicken
Vegeterian ien First
D1-previewsICP
of preview-+OOP Remove Descriptions
of cl-preview
11-rrvieew-ORR IOP, Remove Preferences
1-preview-IQP Remove Diversity
o1 11-rreeewwIP Remove Prateeree and Claude+HQp
Gamini+IQP
GPT-4o+1PP
Llams+IQP
Figure Average emissions for all arms, n 800 across arms. Lower is hetter. Asterisks indicate statistically significant difference (t-test with Bonferroni correction) compared the original menu. Baselines are in green, ablations are in orange. In "ol "ol-preview++QQ Remove Descriptions". ol- ool-prvvse''s descriptions are replaced with simple list of ingredients. In fol-previe++IQP Remove Preferences", the preferences component of the objective (the first term in Problem 2) is removed. In "ol preview+IQP. Remove Diversity", the diversity component of the objective removed, i.e. À = in Problem 2. In "ol -o-preeiiew+IQP IQP, Remove Preferences and Diversity", both the preference and diversity components of the objective Problem are removed, i.e. it becomes feasibility problem.
Data Subset
Expert
Claude 3.5 Sonnet
Gemini 1.5 Pro
GPT-3.5 Turbo
GPT-4o
Ilama
o1 oo-preveew
Nutr. Baseline
All
0.65
0.60
0.41
0.38
0.39
0.51
0.64
0.73
Quartile
0.62
0,43
0.29
0.43
0.33
0,43
0.43
0.71
Quartile 2
0.60
0.65
0.45
0.35
0.40
0.55
0.60
0.80
Quartile
0.77
0.64
0.59
0.41
0.64
0.59
0.68
0.55
Quartile 0.59
0.67
0.29
0.33
0.19
0.48
0.81
Table Comparison performance six LLMs on sensory profile prediction task, specifically the "Overall Satisfaction" dimension, against an expert human food scientist. Statistically significant results (chi-squared test with Bonferroni correction) are in bold,
Claude 3.5 Sonnet
Gemini 1.5 Pro
GPT-3.5 Turbo
GPT--o
Llama
ol -preview
Bascline
Overall Satisfaction
0.43
0.29
0.43
0.33
0.43
0.43
0.71
Mcatiness
0.27
0.33
0.40
0.47
0.27
0.40
0.47
Greasiness
0.65
0,69
0,46
0.77
0.77
0.65
0.69
Juiciness
0.44
0.67
0.67
0.67
0.33
0.61
0.50
Sweetness
0.29
0.71
0.86
0.71
0.71
0.71
0.86
Saltiness
0.75
0.81
0.62
0.81
0.69
0.75
0.56
Purchase
0.60
0.45
0.50
0.50
0.50
0.50
0.45
Dimensions 0.52
0.55
0.53
0.60
0.53
0.57
0.59
Table 15, Accuracies of six LLMs on the sensory profile prediction task, quartile I of the preference gap. Statistically significant results (chi-so (chi-squane squared with Bonferroni correction) are bold.
26

---

What Can Large Language Models Do for Sustainable Food?
Satisfaction
Salisfaction Set Ghoices
2
Original
Expert Cher
Vegetarian Subset
Remove Beel
Beet to Chicken
Vegetarian Finil
of 1-ppreveww-QP
d1-rovlew+HPP. Remove Descriptions
of pl-peelew--PP Remove Preferences
11-praview
of 1-preview-OO.. Remove Diversity
ot prevtewwlle -aP,, Remove Proferances ard Diversily
Claude HOP
GeminisIQP
GPT 4PP-40+1QP
Llama-IQP
Figure 17. Average satisfaction for all arms. 800 across arms. Higher is better. Asterisks indicate statistically significant difference (I-tett with Bonferroni correction) compared the original menu. Baselines are green, ablations in orange. ool-preview IIQ. Remove Descriptions". ol-preview's descriptions are replaced with simple list of ingredients. In "re--prveeew+QPP Remove Preferences". the preferences component component of the objective is removed, i.c. À 0 in Problem 2. In "ol -ol-previe++IQP Remove Preferences and Diversity", both the preference and diversity components of the objective Problem are removed, iee. it becomes a feasibility problem.
objective (the first term in Problem 2) is removed. "ol-prevvew++QQP Remove Diversity", diversity
Claude 3.5 Sonnet
Gemini 1.5 Pro
GPT-3.5 Turbo
GPT-4o
Llama
ol-preview
Baseline
Overall Satisfaction
0.65
0.45
0.35
0.40
0.55
0.60
0.80
Meatiness
0.87
0.80
0.60
0.67
0.60
0.80
0.53
Greasiness
0.52
0.60
0.72
0.72
0.64
0.56
0.68
Juiciness
0.65
0.71
0.71
0.53
0.59
0.59
0.71
Sweetness
0.29
0.43
0.57
0.29
0.14
0,43
0,43
Saltiness
0.67
0.60
0,67
0.60
0.73
0.53
0.60
Purchase
0.62
0.10
0.38
0.38
0.33
0.57
0.57
All Dimensions 0.62
0.52
0.57
0.53
0.54
0.59
0.64
Table 16. Accuracies six LLMS on the sensory profile prediction task, quartile 2 of the preference gap. Statistically significant results square test with Bonferroni correction) are in bold.
27

---

What Can Large Language Models Do for Sustainable Food?
Likelihood of Visiting
8-
Likelhood Visiing
Original
Expert Chef
Vegetarian Subeat
Ramove Beet
Beef Chicken
Vegetarian First
1-preview--QP
1-prview++GG Remove Descriplions
of ol-praviewsiQP Remove Preferences
d1-preview
of prrreview-IOP. Remove Diversity
01 11-prvilew-10 IQP, Remove Preferences
Claudo+1P
GeminH+QP
GPT-40+0P
Liama+1OP
and Diversity
Figure 18. Average likelihood of visiting the hypothetical restaurant for all arms. 800 across arms. Higher is better. Asterisks indicate a statistically significant difference (t-test with Bonferroni correction) compared to original menu. Baselines are in green, ablations in orange. In "ol -preview+IQP. Remove Descriptions", ol --review'ss 's descriptions In "ol Ireviiew+++ Remove Preferences", the preferences component the objective (the first term in Problem 2) is removed. In "ol-preview==QQ. Remove Diversity", the diversity component of the objective removed, i.e. À in Problem 2. In "ol-preview+IIP. Remove Preferences and Diversity",, both the preference and diversity components of the objective in Problem 2 are removed, i.e. becomes feasibility problem.
replaced with simple list of ingredients.
Claude 3.5 Sonnet
Gemini 1.5 Pro
Turbo
GPT-4o
Llama
ol-preview
Baseline
Overall Satisfaction
0.64
0.59
0.41
0.64
0.59
0.68
0.55
Meatiness
0.60
0.60
0.60
0.67
0.60
0.60
0.53
Greasiness
0.65
0.69
0.65
0.85
0.81
0.73
0.77
Juiciness
0.68
0.53
0.58
0.68
0.53
0.79
0.74
Sweetness
0.43
0.43
0.43
0.57
0.43
0.43
0.71
Saltiness
0.69
0.69
0,50
0.81
0.62
0.88
0.88
Purchase
0.55
0.30
0.25
0.45
0.40
0.35
0.75
All Dimensions 0.62
0.56
0.50
0.68
0.59
0.66
0.70
Table 17. Accuracies of six LLMSs the sensory profile prediction task, quartile 3 of the preference gap. Statistically significant results (chi-squared test with Bonferroni correction) are in bold.
28

---

What Can Large Language Models Do for Sustainable Food?
Likelihood Recommending Friend
Likelhood Recommeending Friend
8-
4-
3-
Original
Expert Chef
Vegetarian Subeat
Ramove Beet
Beaf Chicken
Vegetarian First
1-preview--QP
11-review+IGP Remove Descriplions
of ol-praviewsiQP Remove Preferences
d1-preview
of p-rreview-OOP Remove Diversityy
0 21-praview-IOR Remove Preferences
Claudo+1P
GeminH+QP
GPT-40+OP
Liama+1OP
and Diversity
Figure 19. Average likelihood of recommending the hypothetical restaurant to friend for all arms. 800 across arms. Higher is better. Asterisks indicate a statistically significant difference (f-test Bonferroni correction) compared the original menu. Baselines are green, ablations of ingredients. "ol -reviie++QQ. Remove Preferences". the preferences component of the objective (the first term in Problem 2) removed. In "ol-preview++QQ" Remove Diversity". the diversity component of the objective removed, i.e. in Problem 2. In "ol-preview+IQP Remove Preferences and Diversity", both the preference and diversity components of the objective in Problem are removed, i.e. becomes feasibility problem.
orange. In "ol-pree fol-preview+1Q2 Remove Descriptions", ol -rreview''s descriptions replaced with simple list
Claude 3.5 Sonnet
Gemini 1.5 Pro
Turbo
GPT-4o
Llama
ol-preview
Baseline
Overall Satisfaction
0.67
0.29
0.33
0.19
0.48
0.81
0.86
Meatiness
0.67
0.53
0.40
0.73
0.60
0.80
0.40
Greasiness
0.50
0.58
0.69
0.62
0.69
0.62
0.62
Juiciness
0.67
0.50
0.56
0.67
0.61
0.56
0.89
Sweetness
0.43
0,86
0.57
0.86
0.71
1.00
1.00
Saltiness
0.69
0.56
0,50
0.69
0.75
0.81
0.88
Purchase
0.80
0.35
0.35
0.50
0.45
0.75
0.70
All Dimensions 0.64
0.49
0.49
0.57
0.60
0.73
0.74
Table 18. Accuracies of six LLMSs the sensory profile prediction task, quartile 4 of the preference gap. Statistically significant results (chi-squared test with Bonferroni correction) are in bold.
29

---

What Can Large Language Models Do for Sustainable Food?
Animal Weltare
0.25
Animal (animals killed kg food
0.20
0.15
0.10
0.05
0.00
Original
Expert Chef
Vegetorian inn Subset
Remave Beaf
Beef Chicken
Vegetarian First
01 01-prevlewsIOP
01-proview-ICPP Remove Descriptions
previiswwI Remove Preferences
01-preview
of ---revieew-iO. Remove Divereily
31-preview-OOR IOP. Remove Preferences Diversity
Claudo+1QP
Gamink+QQP
GPT 4P+14001OPP
Llama-IOP
Figure 20. Average animal usage for all arms. 800 across arms. Lower is better for animal welfare. Asterisks indicate statistically significant difference (t-test with Bonferroni correction) compared to the original menu. Baselines are in green, ablations are in orange. In "ol -QP- Remove Descriptions", ol-preview's descriptions
replaced with simple list of ingredients. In "ol-preview++OO Remove Preferences", the preferences component of the objective (the first term in Problem 2) is removed. In "ol-preview+#00" Remove Diversity", the diversity component of the objective is removed, ie. = 0 Problem 2. In "ol-preview++QP. Remove Preferences and Diversity", both the preference and diversity components of the objective in Problem 2 are removed, i.c. becomes a fcasibility problem.
Condition
LLM
Q1
Q2
Q3
Q4
Overall
GPT-4o
62.32% %
65.31% %
67.74%
71.43%
64.00%
Claude 3.5 Sonnet
64.79% %
61.22%
58.06%
85.71%
63.20% %
ol-preview
60.92%
57.14%
59.68%
57.14%
59.60%
GPT-3.5 Turbo
55.99%
55.78%
58.06%
85.71%
56.60%
Llama 3.1 70b Instruct
53.17%
48.3%
43.55%
57.14%
50.60%
Gemini 1.5 Pro
50.00%
50.34%
33.87%
28.57%
47.80%
Table 19. Accuracies of six LLMs in the recipe preference prediction task, stratified by the quartiles of the difference in review scores. Statistically significant results (chi-squared test with Bonferroni correction) are in bold.
30

---

What Can Large Language Models Do for Sustainable Food?
Emissions
Emissions (kg CO per kg food iierm)
20
10
Original
Expert Chaf
Vegetarian Subset
Remove Beef
Beef to Chicken
Vegetarien First
of 01-previewslaP
of 11-prviess10f Remove Descriptions
-1-preview-OOR IOP, Remove Preferences
01-preview
of prrvview- Remove Diversity
o prrvveeww Remove Prafe rrferenccs and Diversity
Gamini-QQ
GPT 4o--4o4QP
Liana+IQp
Claude++O)
Figure Average emissions for all arms, excluding vegan and vegetarian participants. 740 across arms. Lower is better. Asterisks indicate a statistically significant difference (t-test with Bonferroni correction) compared to the original menu. Basclines are in green, ablations are in orange. In IIQP Remove Descriptions", ol-preview's 's descriptions are replaced ingredients. In "ol-previee ool-preview+IQP, Remove Preferences". the preferences component of the objective (the first term in Problem 2) is removed. "ol prevvieeee+IPP Remove Diversity", the diversity component objective removed, i.c. in Problem "ol -reevieeiw++Q Remove Preferences and Diversity", both the preference and diversity components of the objective in Problem are removed, i.c. becomes feasibility problem.
simple list
Condition
LLM
Overall
VVs.-VV
Vvs.-V
Vvs. V
V >
-V
V V<-V -V
GPTT-0
64.00 %
70.81
50.77%
37.5%
53.66%
66.03%
Claude 3.5 Sonnet
63.20 %
67.34% %
56.15%
41.67%
43.9%
66.99 %
ol-preview
59.60 %
65.32%
46.15%
50.0%
48.78%
61.72%
GPT-3.5 Turbo
56.60%
63.87% %
42.31%
29.17%
40.24%
59.81 %
Llama 3.1 70b Instruct
50.60%
54.91%
42.31%
33.33%
43.9%
51.91%
Gemini 1.5 Pro
47.80%
50.58%
43.08%
33.33%
45.12%
48.33%
Table 20. Accuracies of six LLMs in the recipe preference prediction task, stratified by the ground truth comparison type: non- vegetarian
vs. non- vegetarian (-Vvs.- vegetarian vs. non- vegetarian (Vvs. -V), and vegetarian vs. vegetarian comparisons (Vvs. V). Within the vegetarian vs. non- vegetarian comparisons, performance is displayed separately when vegetarian preferred (V > -V) and when non- vegetarian is preferred (V < <-)). Statistically significant results (chi-squared test with Bonferroni correction) are bold.
31

---

What Can Large Language Models Do for Sustainable Food?
Satisfaction
Satisfaction Set Choices
6
2-
Original
Expert Chef
Vegetarian Subsel
Remove Beel
Beet Chicken
Vegetarian Fire
of 1-previewHIQP
ol proviee++QPP Remove Descriptions
dt-pravlaw-IQP Remove Preferences
---rrvieww
ot PrpreveewsiPP Remove Diverity
o1 31-pravlew- IQP, Remove Preferances ard Diversily
Claude IDP
GeminisIQP
GPT BPT-40-100 40
Llama=1QP
Figure 22. Average satisfaction for all arms, excluding vegan and vegetarian participants. 740 across arms. Higher is better. Asterisks indicate statistically significant difference (l-test Bonferroni correction) compared the original menu. Baselines are green, ablations are in orange. In "ol-preview+100 Remove Descriptions", ol -preview'ss descriptions replaced with simple list of ingredients. "ol PQPV Remove Preferences", preferences component the objective (the term Problem removed. In "ro-preview++QPP Remove Diversity", the diversity component of the objective is removed, i.e. À 0 in Problem 2. In "ol -IQe Remove Preferences and Diversity", both the preference and diversity components the objective in Problem are removed, i.. it becomes fcasibility problem.
LLM
Accuracy
Claude 3.5 Sonnet
56.90%
Gemini 1.5 Pro
41.38%
GPT-3.5 Turbo GPT--0
55.17%
55.17%
Llama 3.1 70b Instruct
74.14%
ol-preview
68.97%
Table 21. Recipe preference prediction: Post-cut-off dataset. Overall accuracy the post-cut-off dataset.
32

---

What Can Large Language Models Do for Sustainable Food?
Likelihood of Visiting
Likel Visiting
4
2-
0
Original
Expert Chef
Vegetarian Subsat
Remove Beef
Beef to Chicken
Vegetanan First
-1-preview-IPP
o D1-praview-IQP Remove Descriptions
ot ot-proview+HOr HOP, Remova Preferences
01-preview
of -r-review--OP. Remove Diversity
of pravvlle HOP, Remove Preferences
ClaudevIOp
Gemink-QQP
GPT GPT-40-10P
Llama-1OP
and Diversity
Figure 23. Average likelihood of visiting the hypothetical restaurant for all arms, excluding vegan and vegetarian participants. 740 across arms. Higher is better. Asterisks indicate statistically significant difference (t-test with Bonferroni correction) compared to the original menu. Baselines are in green, ablations are orange. "ol- pr-preview++QR. Remove Descriptions", ol o1-preview's descriptions are replaced with simple list of ingredients. In "ol "ol-preview++QQ Remove Preferences". the preferences component of the objective removed. "ol-preview+IQP. Remove Diversity", diversity component of objective removed, i.e. X 0 in Problem 2. In "ol-pre "ol-preview+IQU Remove Preferences and Diversity". both the preference and diversity components of the objective Problem are removed, i.c. becomes feasibility problem.
(the first term in Problem
33

---

What Can Large Language Models Do for Sustainable Food?
Likelihood Recommending to a Friend
Likelhoodd Recommandding to Fiiend
6-
4
3-
2-
1
Original
Expert Chef
Vegetarian Subsat
Remove Beef
Beef to Chicken
Vegetanan First
-1-preview-IPP
o 11-praview-IQP Remove Descriptions
ot dt-rrviee=HOr HQP, Remova Preferences
01-preview
of Prpreview--OP. Remove Diversity
p-aavllew HOP, Remove Preferences and Diversity
ClaudevIOp
Gemink-QQP
GPT GPT-40-10P
Llama-IOP
Figure 24. Average likelihood of recommending the hypothetical restaurant friend for all arms, excluding vegan and vegetarian participants. 740 across arms. Higher is better. Asterisks indicate statistically significant difference (t-test with Bonferroni correction) compared the original menu. Baselines are green, ablations are orange. In -rrviie=+IQR, Remove Descriptions", ol ol-revissw's 's descriptions component of objective (the term Problem 2) is removed. In "ol-preview++IP. Remove Diversity". diversity component the objective is removed, i.e. À = 0 in Problem 2. In "ol rol-preview+IQP. Remove Preferences and Diversity", both the preference and diversity components the objective Problem are removed, i.c. becomes a feasibility problem.
replaced with simple list of ingredients. In "ol -reeevieew++QQP Remove Preferences", the preferences
34

---

What Can Large Language Models Do for Sustainable Food?
Animal Welfare
0.25
Animal (animals killed kgg of food
0.20
0.00
Original
Expert Chef
Vegetarian Subser
Remove Beef
Beaf to Chickan
Vegetarian First
01-preview-IQP
of ot-praviowsICE Remove Deseriptions
of d1-review-IQP IQP, Remove Preferences
01-preview
01-preview-IOPP Remove Diversity
11-preview-te HOP, Remove Preferences Diversity
Claude
Gemini-IQP
GPT 4o--40-10P
Llama-1Qp
Figure 25. Average animal usage for all arms, excluding vegan and vegetarian participants. 7410 across Lower is Asterr.Asteiskss indicate a statistically significant difference (t-test with Bonferroni correction) compared to the original menu. Baselines are in green, ablations are in orange. In pol-preview+IQP. Remove Descriptions", ol ol-preview's 's descriptions are replaced with simple list of ingredients. In "ol-preview++IP. Remove Preferences". the preferences component of the objective (the first term in Problem 2) is removed. pol-peeiie++QP.. Remove Diversity", the diversity component objective removed, i.e. "ol preevieeww+ Remove Preferences and Diversity". both the preference and diversity components of the objective in Problem are removed, i.c. becomes feasibility problem.
in Problem
35

---

What Can Large Language Models Do for Sustainable Food?
F. Menus
Original Menu (Banerjee et al., 2023)
1. Chicken Curry Ramen. Japanese fried chicken & noodles in a delicious curry broth.
2. Pork Ribs. Pork Ribs smothered with Kentucky style BBQ sauce.
3. Beef Bourguignon. 6oz Black Angus beef burger topped with crispy bacon, red wine braised onions, Raclette cheese.
4. Chicken Katsu Curry. Succulent chicken erispy Japanese panko crumb with mild eurry sauce, pickles and steamed rice.
5. Pepperoni Melt. This one's got our triple cheese blend, tomato sauce and pepperoni.
6. Lamb Meatballs with Spaghetti. Handmade lamb patties grilled and topped with Greek yoghurt, tomato sauce, and onions.
7. Aromatic Duck Rolls. Aromatic roast duck, hoisin sauce, cucumber, spring onion, rolled in rice paper.
8. Slow Cooked Beef in Chianti Sauce. Beef shin and shallots slow cooked in rich Chianti red wine sauce with a cheesy dumpling.
9. Pork Porchetta. Slow-roasted pork belly in herbed red wine sauce, served with roasted new potatoes and broccoli.
10. Cured Salmon Sashimi. Dazzling beetroot cured slices of salmon served with lemon.
11. Beef Brisket and Venison Tagliatelle. A beef, brisket, venison, red wine and beef dripping ragu with tagliatelle, fresh bufala mozzarella and crispy sage.
12. Rustica Chorizo Pizza. Chorizo salami, torn wild boar and pork meatballs, smoked mozzarella and baby sunblush
tomatoes.
13. Butterfly Chicken Burger. Two succulent chicken breasts joined by crispy skin, packaged in Portuguese roll and topped with tomato and lettuce.
14. American Hot Pizza. Pepperoni, mozzarella and tomato, with your choice of hot green, Roquito jalapeno peppers.
15. Beef Angus Burger. 6oz beef burger with baby gem lettuce and plum tomato in a chargrilled brioche bun with Dijon
mayonnaise.
16. Beer Battered Fish with Chips. with frites and pea & mint puree.
17. Wild Boar Polpette. Oven-baked herby wild boar and pork meatballs in pomodoro sauce with melted smoked mozzarella.
18. Steak. Sliced Black Angus rump steak with red onion chutney, watercress, and Dijon mayonnaise.
19. Canelloni. pasta with béchamel, mozzarella and Gran Milano cheese.
20. Supreme Pizza. Cheese and tomato, onion, mushroom, fresh basil, olive and garlic oil.
21. Goat's Cheese Calzone. Goats cheese, grilled aubergines, roasted peppers, oven roasted tomatoes.
22. Vegan Spaghetti Lentil Ragu. A hearty ragu of green lentils and mixed vegetables in a rich tomato and fennel sauce.
23. Jack Fruit Arrabiata. Vegan jackfruit peppers, spicy harissa, roquito chilis vegan Mozzarella cheese, finished with basil.
24. Halloumi Sticks with Mayo Avocado Dip. Chunky sticks of grilled halloumi cheese with a chilli jam and mayo avocado dip.
25. Cheese Salad. Smoked cheddar, cheddar and sage stuffing.
26. Falafel with Tahini. Our signature recipe, served with tahini dip.
27. Four Cheese Margherita. Mozzarella, mascarpone, fontal and grana cheese on a tomato base.
28. Vegan Meatballs. Vegan meatballs in a rich tomato and fennel sauce.
29. Bufala Caprese. Specialty tomatoes and drizzle of roasted garlic extra virgin olive oil with Buffalo mozzarella.
30. Butternut Squash Salad. Roasted butternut squash with feta cheese, pomegranate seeds, mixed leaf salad and watercress.
31. Panchetta Carbonara. crispy pancetta and asparagus in a velvety sauce made with mascarpone, pecorino and Grana
Padano cheese.
36

---

What Can Large Language Models Do for Sustainable Food?
32. Lentil Linguine Ragu. Rich Italian lentil ragu with baby spinach, tomatoes, basil & pecorino cheese.
33. Aegean Slaw. Thinly shredded cabbage, carrot and red onion peppers, with an olive oil dressing.
34. Goat's Cheese Salad. Goat's cheese and balsamic onion crostinis on winter baby kale, red pepper, eucumber, plum tomatoes.
35. Harusami Aubergine. Fried slices of aubergine with garlic & ginger sesame soy.
36. Inari Taco. Golden tofu pockets filled with sticky sushi rice, avocado salsa & vegan sriracha mayo.
Expert Chef
1. Crispy Miso Eggplant Ramen. Miso marinated eggplant in a crispy panko crust served with a shitake rich curry broth and hand pulled ramen noodles.
2. Pork Ribs. Pork Ribs smothered with Kentucky style BBQ sauce.
3. Beef Bourguignon. 6oz Black Angus beef burger topped with crispy bacon, red wine braised onions, Raclette cheese.
4. Chicken Katsu Curry. Succulent chicken crispy Japanese panko crumb with mild curry sauce, pickles and steamed rice.
5. Pepperoni Melt. This one's got our triple cheese blend, tomato sauce and pepperoni.
6. Lamb Meatballs with Spaghetti. Handmade lamb patties grilled and topped with Greek yoghurt, tomato sauce, and onions.
7. Star Anise Black Bean Tempeh Rolls. Black bean tempeh with a lacquered star anise hoisin sauce layered with pickled cucumber, shredded carrots, mint and maple toasted sesame seeds wrapped in rice paper.
8. Basque Celeriac and Gigante Bean Ragout. Celeriac, Gigante beans, nantes carrots, baby leeks, spring garlic, thyme, bay leaves and a lacto fermented tomato sauce creates an orchestra of flavors and textures.
9. Pork Porchetta. Slow-roasted pork belly in herbed red wine sauce, served with roasted new potatoes and broccoli.
10. Beet and Tamari Infused "Tuna". Watermelon infused with Tamari and beet juice, dehydrated and served beet horseradish puree with baby watercress, toasted tamari sunflower seeds and yuzu dressing.
11. Beef Brisket and Venison Tagliatelle. A beef, brisket, venison, red wine and beef dripping ragu with tagliatelle, fresh bufala mozzarella and crispy sage.
12. Rustica Soyrizo Flatbread. Smoked paprika Soyrizo, toasted pinenut basil pesto slathered on extra virgin olive oil herb brushed house flatbread with confit sun dried tomato and garlic, spicy arugula.
13. Butterfly Chicken Burger. Two succulent chicken breasts joined by crispy skin. packaged in a Portuguese roll and topped with tomato and lettuce.
14. American Hot Pizza. Pepperoni, mozzarella and tomato, with your choice of hot green, Roquito or jalapeno peppers.
15. Beef Angus Burger. 60z beef burger with baby gem lettuce and plum tomato in a chargrilled brioche bun with Dijon mayonnaise.
16. Beer Battered Shio Koji Marinated Enoki Mushroom. Shio koji marinated enoki mushroom, Okinawa sweet potatoes and Kabocha squash tempura with a fermented garlic dipping sauce.
17. Wild Boar Polpette. Oven-baked herby wild boar and pork meatballs in pomodoro sauce with melted smoked mozzarella.
18. Steak. Sliced Black Angus rump steak with red onion chutney, watercress, and Dijon mayonnaise.
19. Pasta olio ceci. cast iron sauteed garlic, chilli flakes and local pressed extra virgin olive oil tossed with al dente spaghetti and finished with pasta water and smashed ceci Igarbanzo beans] then garnished with heaps of chopped Italian parsley and ground black pepper and a squeeze of Meyer lemon.
20. Supreme Pizza. Hand thrown sourdough pizza with fermented tomato basil sauce, , wood grilled mushrooms and herbed cashew cheese.
21. Goat's Cheese Calzone. Goats cheese, grilled aubergines, roasted peppers, oven roasted tomatoes.
22. Vegan Spaghetti Lentil Ragu. A hearty ragu of green lentils and mixed vegetables in a rich tomato and fennel sauce.
37

---

What Can Large Language Models Do for Sustainable Food?
23. Jack Fruit Arrabiata. Vegan jackfruit peppers, spicy harissa, roquito chilis & vegan Mozzarella cheese, finished with basil.
24. Halloumi Sticks with Mayo Avocado Dip. Chunky sticks of grilled halloumi cheese with a chilli jam and mayo avocado dip.
25. Cheese Salad. Smoked cheddar, cheddar and sage stuffing.
26. Falafel with Tahini. Our signature recipe, served with a tahini dip.
27. Four Cheese Margherita. Mozzarella, mascarpone, fontal and grana cheese on a tomato base.
28. Vegan Meatballs. Vegan meatballs in a rich tomato and fennel sauce.
29. Bufala Caprese. Specialty tomatoes and drizzle of roasted garlic in extra virgin olive oil with Buffalo mozzarella.
30. Butternut Squash Salad. Roasted pomegranate molasses rubbed butternut squash tossed with pomegranate arils, toasted pumpkin seeds, herbed macadamia nut cheese with mushroom flat bread crumble.
31. Panchetta Carbonara. crispy pancetta and asparagus in velvety sauce made with mascarpone, pecorino and Grana Padano cheese.
32. Pasta lenticchie. Beluga lentil ragout slow cooked with Italian plum tomatoes ,tossed with bronze linguine and finished with smoky Shiitake bacon.
33. Aegean Slaw. Thinly shredded cabbage, carrot and red onion peppers, with an olive oil dressing.
34. Goat's Cheese Salad. Goat's cheese and balsamic onion crostinis on winter baby kale, red pepper, cucumber, plum tomatoes.
35. Harusami Aubergine. Fried slices of aubergine with garlic & ginger sesame soy.
36. Inari Taco. Golden tofu pockets filled with sticky sushi rice, avocado salsa vegan sriracha mayo.
Remove Beef
1. Chicken Curry Ramen. Japanese fried chicken & noodles in a delicious curry broth.
2. Pork Ribs. Pork Ribs smothered with Kentucky style BBQ sauce.
3. Chicken Katsu Curry. Succulent chicken in a crispy Japanese panko crumb with mild curry sauce, pickles and steamed rice.
4. Pepperoni Melt. This one's got our triple cheese blend, tomato sauce and pepperoni.
5. Lamb Meatballs with Spaghetti. Handmade lamb patties grilled and topped with Greek yoghurt, tomato sauce, and onions.
6. Aromatic Duck Rolls. Aromatic roast duck, hoisin sauce, cucumber, spring onion, rolled in rice paper.
7. Pork Porchetta. Slow-roasted pork belly in herbed red wine sauce, served with roasted new potatoes and broccoli.
8. Cured Salmon Sashimi. Dazzling beetroot cured slices salmon served with lemon.
9. Rustica Chorizo Pizza. Chorizo salami, torn wild boar and pork meatballs, smoked mozzarella and baby sunblush tomatoes.
10. Butterfly Chicken Burger. Two succulent chicken breasts joined by crispy skin, packaged in a Portuguese roll and topped with tomato and lettuce.
11. American Hot Pizza. Pepperoni, mozzarella and tomato, with your choice of hot green, Roquito or jalapeno peppers.
12. Beer Battered Fish with Chips. with frites and pea & mint puree.
13. Wild Boar Polpette. Oven-baked herby wild boar and pork meatballs in pomodoro sauce with melted smoked
mozzarella.
14. Canelloni. pasta with béchamel, mozzarella and Gran Milano cheese.
15. Supreme Pizza. Cheese and tomato, onion, mushroom, fresh basil, olive and garlic oil.
16. Goat's Cheese Calzone. Goats cheese, grilled aubergines, roasted peppers, roasted tomatoes.
17. Vegan Spaghetti Lentil Ragu. A hearty ragu of green lentils and mixed vegetables in rich tomato and fennel sauce.
38

---

What Can Large Language Models Do for Sustainable Food?
18. Jack Fruit Arrabiata. Vegan jackfruit peppers, spicy harissa, roquito chilis & vegan Mozzarella cheese, finished with basil.
19. Halloumi Sticks with Mayo Avocado Dip. Chunky sticks of grilled halloumi cheese with a chilli jam and mayo avocado dip.
20. Cheese Salad. Smoked cheddar, cheddar and sage stuffing.
21. Falafel with Tahini. Our signature recipe, served with tahini dip.
22. Four Cheese Margherita. Mozzarella, mascarpone, fontal and grana cheese on a tomato base.
23. Vegan Meatballs. Vegan meatballs in a rich tomato and fennel sauce.
24. Bufala Caprese. Specialty tomatoes and drizzle of roasted garlic in extra virgin olive oil with Buffalo mozzarella.
25. Butternut Squash Salad. Roasted butternut squash with feta cheese, pomegranate seeds, mixed Icaf salad and watereress.
26. Panchetta Carbonara. crispy pancetta and asparagus in velvety sauce made with mascarpone, pecorino and Grana Padano cheese.
27. Lentil Linguine Ragu. Rich Italian lentil ragu with baby spinach, tomatoes, basil & pecorino cheese.
28. Aegean Slaw. Thinly shredded cabbage, carrot and red onion peppers, with an olive oil dressing.
29. Goat's Cheese Salad. Goat's cheese and balsamic onion crostinis winter baby kale, red pepper, cucumber, plum tomatoes.
30. Harusami Aubergine. Fried slices aubergine with garlic & ginger sesame soy.
31. Inari Taco. Golden tofu pockets filled with sticky sushi rice, avocado salsa vegan sriracha mayo.
Vegetarian
1. Canelloni. pasta with béchamel, mozzarella and Gran Milano cheese.
2. Supreme Pizza. Cheese and tomato, onion, mushroom, fresh basil, olive and garlic oil.
3. Goat's Cheese Calzone. Goats cheese, grilled aubergines, roasted peppers, oven roasted tomatoes.
4. Vegan Spaghetti Lentil Ragu. A hearty ragu green lentils and mixed vegetables in a rich tomato and fennel sauce.
5. Jack Fruit Arrabiata. Vegan jackfruit peppers, spicy harissa, roquito chilis & vegan Mozzarella cheese, finished with basil.
6. Halloumi Sticks with Mayo Avocado Dip. Chunky sticks of grilled halloumi cheese with a chilli jam and mayo avocado dip.
7. Cheese Salad. Smoked cheddar, cheddar and sage stuffing.
8. Falafel with Tahini. Our signature recipe, served with tahini dip.
9. Four Cheese Margherita. Mozzarella, mascarpone, fontal and grana cheese on a tomato base.
10. Vegan Meatballs. Vegan meatballs in a rich tomato and fennel sauce.
11. Bufala Caprese. Specialty tomatoes and drizzle of roasted garlic in extra virgin olive oil with Buffalo mozzarella.
12. Butternut Squash Salad. Roasted butternut squash with feta cheese, pomegranate seeds, mixed leaf salad and watercress.
13. Lentil Linguine Ragu. Rich Italian lentil ragu with baby spinach, tomatoes, basil & pecorino cheese.
14. Aegean Slaw. Thinly shredded cabbage, carrot and red onion peppers, with an olive oil dressing.
15. Goat's Cheese Salad. Goat's cheese and balsamic onion crostinis on winter baby kale, red pepper, cucumber, plum tomatoes.
16. Harusami Aubergine. Fried slices of aubergine with garlic & ginger sesame soy.
17. Inari Taco, Golden tofu pockets filled with sticky sushi rice, avocado salsa & vegan sriracha mayo.
39

---

What Can Large Language Models Do for Sustainable Food?
Beef to Chicken
1.
Chicken Curry Ramen. Japanese fried chicken & noodles in delicious curry broth.
2. Pork Ribs. Pork Ribs smothered with Kentucky style BBQ sauce.
3. Chicken Bourguignon. 6oz chicken burger topped with crispy bacon, red wine braised onions, Raclette cheese.
4. Chicken Katsu Curry. Succulent chicken crispy Japanese panko crumb with mild curry sauce, pickles and steamed rice.
5. Pepperoni Melt. This one's got our triple cheese blend, tomato sauce and pepperoni.
6. Lamb Meatballs with Spaghetti. Handmade lamb patties grilled and topped with Greek yoghurt, tomato sauce, and onions.
7. Aromatic Duck Rolls. Aromatic roast duck, hoisin sauce, cucumber, spring onion, rolled in rice paper.
8. Slow Cooked Chicken in Chianti Sauce. Chicken and shallots slow cooked in rich Chianti red wine sauce with cheesy dumpling.
9. Pork Porchetta. Slow-roasted pork belly in herbed red wine sauce, served with roasted new potatoes and broccoli.
10. Cured Salmon Sashimi. Dazzling beetroot cured slices salmon served with lemon.
11. Chicken and Venison Tagliatelle. chicken breast, venison, red wine and beef dripping ragu with tagliatelle, fresh bufala mozzarella and crispy sage.
12. Rustica Chorizo Pizza. Chorizo salami, torn wild boar and pork meatballs, smoked mozzarella and baby sunblush tomatoes.
13. Butterfly Chicken Burger. Two succulent chicken breasts joined by crispy skin, packaged in a Portuguese roll and topped with tomato and lettuce.
14. American Hot Pizza. Pepperoni, mozzarella and tomato, with your choice of hot green, Roquito or jalapeno peppers.
15. Chicken Burger. 6oz chicken burger with baby gem lettuce and plum tomato in a chargrilled brioche bun with Dijon mayonnaise.
16. Beer Battered Fish with Chips. with frites and pea mint puree.
17. Wild Boar Polpette. Oven-baked herby wild boar and pork meatballs in pomodoro sauce with melted smoked mozzarella.
18. Baked Chicken. Baked chicken with red onion chutney, watercress, and Dijon mayonnaise.
19. Canelloni. pasta with béchamel, mozzarella and Gran Milano cheese.
20. Supreme Pizza. Cheese and tomato, onion, mushroom, fresh basil, olive and garlic oil.
21. Goat's Cheese Calzone. Goats cheese, grilled aubergines, roasted peppers, oven roasted tomatoes.
22. Vegan Spaghetti Lentil Ragu. A hearty ragu of green lentils and mixed vegetables in a rich tomato and fennel sauce.
23. Jack Fruit Arrabiata. Vegan jackfruit peppers, spicy harissa, roquito chilis & vegan Mozzarella cheese, finished with basil.
24. Halloumi Sticks with Mayo Avocado Dip. Chunky sticks of grilled halloumi cheese with a chilli jam and mayo avocado dip.
25. Cheese Salad. Smoked cheddar, cheddar and sage stuffing.
26. Falafel with Tahini. Our signature recipe, served with tahini dip.
27. Four Cheese Margherita. Mozzarella, mascarpone, fontal and grana cheese tomato base.
28. Vegan Meatballs. Vegan meatballs in a rich tomato and fennel sauce.
29. Bufala Caprese. Specialty tomatoes and drizzle of roasted garlic in extra virgin olive oil with Buffalo mozzarella.
30. Butternut Squash Salad. Roasted butternut squash with feta cheese, pomegranate seeds, mixed leaf salad and watercress.
31. Panchetta Carbonara. crispy pancetta and asparagus in a velvety sauce made with mascarpone, pecorino and Grana Padano cheese.
32. Lentil Linguine Ragu. Rich Italian lentil ragu with baby spinach, tomatoes, basil & pecorino cheese.
40

---

What Can Large Language Models Do for Sustainable Food?
33. Aegean Slaw. Thinly shredded cabbage, carrot and red onion peppers, with olive oil dressing.
34. Goat's Cheese Salad. Goat's cheese and balsamic onion crostinis on winter baby kale, red pepper, eucumber, plum tomatoes.
35. Harusami Aubergine. Fried slices of aubergine with garlic & ginger sesame soy.
36. Inari Taco. Golden tofu pockets filled with sticky sushi rice, avocado salsa vegan sriracha mayo.
Vegetarian First
1. Canelloni. Pasta with béchamel, mozzarella and Gran Milano cheese.
2. Supreme Pizza. Cheese and tomato, onion, mushroom, fresh basil, olive and garlic oil.
3. Goat's Cheese Calzone. Goats cheese, grilled aubergines, roasted peppers, oven roasted tomatoes.
4. Vegan Spaghetti Lentil Ragu. A hearty ragu of green lentils and mixed vegetables in a rich tomato and fennel sauce.
5. Jack Fruit Arrabiata. Vegan jackfruit peppers, spicy harissa, roquito chilis vegan Mozzarella cheese, finished with basil.
6. Halloumi Sticks with Mayo Avocado Dip. Chunky sticks of grilled halloumi cheese with a chilli jam and mayo avocado dip.
7. Cheese Salad. Smoked cheddar, cheddar and sage stuffing.
8. Falafel with Tahini. Our signature recipe, served with tahini dip.
9. Four Cheese Margherita. Mozzarella, mascarpone, fontal and grana cheese on a tomato base.
10. Vegan Meatballs. Vegan meatballs in a rich tomato and fennel sauce.
11. Bufala Caprese. Specialty tomatoes and drizzle of roasted garlic extra virgin olive oil with Buffalo mozzarella.
12. Butternut Squash Salad. Roasted butternut squash with feta cheese, pomegranate seeds, mixed leaf salad and watercress.
13. Lentil Linguine Ragu. Rich Italian lentil ragu with baby spinach, tomatoes, basil & pecorino cheese.
14. Aegean Slaw. Thinly shredded cabbage, carrot and red onion peppers, with an olive oil dressing.
15. Goat's Cheese Salad. Goat's cheese and balsamic onion crostinis on winter baby kale, red pepper, cucumber, plum tomatoes.
16. Harusami Aubergine. Fried slices of aubergine with garlic & ginger sesame soy.
17. Inari Taco. Golden tofu pockets filled with sticky sushi rice, avocado salsa & vegan sriracha mayo.
18. Chicken Curry Ramen. Japanese fried chicken & noodles in delicious curry broth.
19. Pork Ribs. Pork Ribs smothered with Kentucky style BBQ sauce.
20. Beef Bourguignon. 6oz Black Angus beef burger topped with crispy bacon, red wine braised onions, Raclette cheese.
21. Chicken Katsu Curry. Succulent chicken crispy Japanese panko crumb with mild eurry sauce, pickles and steamed rice.
22. Pepperoni Melt. This one's got our triple cheese blend, tomato sauce and pepperoni.
23. Lamb Meatballs with Spaghetti. Handmade lamb patties grilled and topped with Greek yoghurt, tomato sauce, and onions.
24. Aromatic Duck Rolls. Aromatic roast duck, hoisin sauce, cucumber, spring onion, rolled in rice paper.
25. Slow Cooked Beef in Chianti Sauce. Beef shin and shallots slow cooked in rich Chianti red wine sauce with a cheesy dumpling.
26. Pork Porchetta. Slow-roasted pork belly in herbed red wine sauce, served with roasted new potatoes and broccoli.
27. Cured Salmon Sashimi. Dazzling bectroot cured slices salmon served with lemon.
28. Beef Brisket and Venison Tagliatelle. A beef, brisket, venison, red wine and beef dripping ragu with tagliatelle, fresh bufala mozzarella and crispy sage.
29. Rustica Chorizo Pizza. Chorizo salami, torn wild boar and pork meatballs, smoked mozzarella and baby sunblush tomatoes.

---

What Can Large Language Models Do for Sustainable Food?
30. Butterfly Chicken Burger. Two succulent chicken breasts joined by crispy skin, packaged in Portuguese roll and topped with tomato and lettuce.
31. American Hot Pizza. Pepperoni, mozzarella and tomato, with your choice of hot green, Roquito or jalapeno peppers.
32. Beef Angus Burger. 6oz beef burger with baby gem lettuce and plum tomato in a chargrilled brioche bun with Dijon mayonnaise.
33. Beer Battered Fish with Chips. With frites and pea & mint puree.
34. Wild Boar Polpette. Oven-baked herby wild boar and pork meatballs in pomodoro sauce with melted smoked
mozzarella.
35. Steak. Sliced Black Angus rump steak with red onion chutney, watercress, and Dijon mayonnaise.
36. Panchetta Carbonara. Crispy pancetta and asparagus in a velvety sauce made with mascarpone, pecorino and Grana Padano cheese.
o1- -r-reviee+IQP
1. Creamy Mushroom Tagliatelle. An indulgent pasta dish featuring sautéed mushrooms and baby spinach in creamy mascarpone sauce, tossed with tagliatelle and seasoned with garlic oil and fresh basil.
2. Vegetable Delight Pizza. A delicious crispy pizza topped with mozzarella, tomato sauce, and medley of grilled vegetables, finished with fresh basil.
3. Three Cheese Omelette. A fluffy omelette loaded with smoked cheddar, mozzarella, grana cheese, and sautéed red onions.
4. Eggplant Parmesan. Layers of tender aubergine baked with rich tomato sauce, melted mozzarella, and Grana Padano cheese, garmished with fresh basil.
5. Mushroom and Goat's Cheese Omelette. A fluffyy omelette filled with sautéed mushrooms and creamy goat's 's cheese, infused with garlic oil and fresh basil.
6. Chickpea Curry with Rice. flavorful and hearty chickpea curry served with steamed rice and accompanied by tangy pickles.
7. Spinach and Feta Stuffed Mushrooms. Large mushrooms stuffed with sautéed baby spinach and creamy feta cheese, drizzled with garlic oil and baked to perfection.
8. Lentil Veggie Burger. A hearty lentil-based veggic burger served on a toasted brioche bun with fresh lettuce, tomato, and tangy Dijon mayonnaise.
9. Falafel Salad. Our signature falafel served over a fresh mixed salad, with crunchy cucumbers, juicy tomatoes, red onions, and a creamy tahini dressing.
10. Egg Shakshuka. Poached eggs simmered in a spiced tomato sauce with peppers and onions, garnished with fresh basil.
11. Vegetable and Tofu Stir-Fry. A vibrant stir-fry of crispy tofu and fresh vegetables tossed with noodles in savory garlic and ginger sesame soy sauce.
12. Tofu Katsu Curry. Suceulent tofu coated in crispy panko crumbs, served with mild curry sauce, tangy pickles, and steamed rice.
13. Lentil Stuffed Peppers. Roasted bell peppers stuffed with hearty lentils, tomato sauce, and fresh baby spinach, topped with creamy goat's cheese.
14. Butternut Squash and Feta Salad. Sweet roasted butternut squash and tangy feta cheese on bed of fresh mixed greens, sprinkled with pomegranate seeds and watercress.
15. Vegan Meatball Sub. Hearty vegan meatballs simmered in pomodoro sauce. topped with melted smoked mozzarella, served in toasted Portuguese roll.
16. Chickpea and Spinach Curry. nourishing curry of chickpeas and baby spinach simmered in a mild curry sauce, served with steamed rice.
17. Tofu Curry Ramen. Japanese-style ramen with fried tofu and noodles in a delicious curry broth, topped with pak choi and pickled onions.
42

---

What Can Large Language Models Do for Sustainable Food?
18. Mushroom and Lentil Bolognese. Hearty mushrooms and lentils cooked in rich tomato sauce, served tagliatelle pasta and garnished with fresh basil.
19. Supreme Pizza. Cheese and tomato, onion, mushroom, fresh basil, olive and garlic oil.
20. Chicken Curry Ramen. Japanese fried chicken & noodles in a delicious curry broth.
21. Pork Ribs. Pork Ribs smothered with Kentucky style BBQ sauce.
22. Butterfly Chicken Burger. Two succulent chicken breasts joined by crispy skin, packaged in a Portuguese roll and topped with tomato and lettuce.
23. Four Cheese Margherita. Mozzarella, mascarpone, fontal and grana cheese on a tomato base.
24. Panchetta Carbonara. crispy pancetta and asparagus in a velvety sauce made with mascarpone, pecorino and Grana
Padano cheese.
25. Chicken Katsu Curry. Succulent chicken in crispy Japanese panko crumb with mild curry sauce, pickles and steamed rice.
26. Rustica Chorizo Pizza. Chorizo salami, torn wild boar and pork meatballs, smoked mozzarella and baby sunblush tomatoes.
27. Beer Battered Fish with Chips. with frites and pea & mint puree.
28. Pork Porchetta. Slow-roasted pork belly in herbed red wine sauce, served with roasted new potatoes and broccoli.
29. Butternut Squash Salad. Roasted butternut squash with feta cheese, pomegranate seeds, mixed leaf salad and watercress.
30. Aegean Slaw. Thinly shredded cabbage, carrot and red onion peppers, with olive oil dressing.
31. Cured Salmon Sashimi. Dazzling beetroot cured slices salmon served with lemon.
32. Falafel with Tahini. Our signature recipe, served with tahini dip.
33. Lentil Linguine Ragu. Rich Italian lentil ragu with baby spinach, tomatoes, basil & pecorino cheese.
34. Aromatic Duck Rolls. Aromatic roast duck, hoisin sauce, cucumber, spring onion, rolled in rice paper.
35. Vegan Spaghetti Lentil Ragu. A hearty ragu of green lentils and mixed vegetables in a rich tomato and fennel sauce.
36. Harusami Aubergine. Fried slices of aubergine with garlic & ginger sesame soy.
GPT-40+IQP
1. Falafel Wrap. A flavorful and nutritious wrap perfect for any time of day.
2. Lentil Meatball Spaghetti. A comforting combination of lentil meatballs and rich tomato sauce with pasta.
3. Eggs Florentine Pizza. A delightful pizza featuring fresh eggs and vibrant toppings.
4. Spinach and Asparagus Carbonara. A creamy and delicious pasta loaded with greens.
5. Butternut Squash and Feta Cannelloni. Rich, creamy, and packed with flavor, perfect for vegetarians.
6. Roasted Beetroot and Goat's Cheese Salad. vibrant salad with the earthy sweetness of beetroot and creamy goat's cheese.
7. BBQ Jackfruit Ribs. Tender jackfruit smothered in tangy BBQ sauce, served with sides.
8. Eggplant Ragu Tagliatelle. hearty and delicious pasta with a rich eggplant ragu.
9. Vegan Stuffed Bell Peppers. Hearty stuffed peppers, packed with delicious and healthy fillings.
10. Spicy Lentil and Jackfruit Tacos. fiery taco loaded with protein and bold flavors.
11. Mushroom Bourguignon. A rich and savory dish, perfect for a comforting meal.
12. Tofu Porchetta. Herb-crusted tofu and flavorful sides for a vegan take on porchetta.
13. Chickpea Katsu Burger. A satisfying burger with crunchy chickpea patty and fresh toppings.
14. Panko Crusted Tofu Fingers. Crunchy tofu sticks with a deliciously creamy dip.
15. Tofu and Veggie ftyr-fry. A nutritious stir-fry with tofu and mix of fresh vegetables.
16. Pork Ribs. Pork Ribs smothered with Kentucky style BBQ sauce.
43

---

What Can Large Language Models Do for Sustainable Food?
17. Beer Battered Fish with Chips. with frites and pea & mint puree.
18. Supreme Pizza. Cheese and tomato, onion, mushroom, fresh basil, olive and garlic oil.
19. Four Cheese Margherita. Mozzarella, mascarpone, fontal and grana cheese on tomato base.
20. Panchetta Carbonara. crispy pancetta and asparagus in velvety sauce made with mascarpone, pecorino and Grana Padano cheese.
21. Chicken Curry Ramen. Japanese fried chicken & noodles in a delicious curry broth.
22. Pork Porchetta. Slow-roasted pork belly in herbed red wine sauce, served with roasted new potatoes and broccoli.
23. Rustica Chorizo Pizza. Chorizo salami, torn wild boar and pork meatballs, smoked mozzarella and baby sunblush tomatoes.
24. Goat's Cheese Salad. Goat's cheese and balsamic onion crostinis winter baby kale, red pepper, cucumber, plum tomatoes.
25. Chicken Katsu Curry. Succulent chicken in crispy Japanese panko crumb with mild eurry sauce, pickles and steamed rice.
26. Aromatic Duck Rolls. Aromatic roast duck, hoisin sauce, cucumber, spring onion, rolled in rice paper.
27. Wild Boar Polpette. Oven-baked herby wild boar and pork meatballs in pomodoro sauce with melted smoked mozzarella.
28. Aegean Slaw. Thinly shredded cabbage, carrot and red onion peppers, with an olive oil dressing.
29. Cured Salmon Sashimi. Dazzling beetroot cured slices of salmon served with lemon.
30. Jack Fruit Arrabiata. Vegan jackfruit peppers, spicy harissa, roquito chilis vegan Mozzarella cheese, finished with basil.
31. Butternut Squash Salad. Roasted butternut squash with feta cheese, pomegranate seeds, mixed Icaf salad and watercress.
32. Lentil Linguine Ragu. Rich Italian lentil ragu with baby spinach, tomatoes, basil & pecorino cheese.
33. Vegan Spaghetti Lentil Ragu. A hearty ragu of green lentils and mixed vegetables in rich tomato and fennel sauce.
34. Vegan Meatballs. Vegan meatballs in a rich tomato and fennel sauce.
35. Falafel with Tahini. Our signature recipe, served with a tahini dip.
36. Harusami Aubergine. Fried slices of aubergine with garlic & ginger sesame soy.
Claude 3.5 Sonnet+IQP
1. Cheese Melt Pizza. A gooey, cheesy pizza topped with savory mushrooms and tangy tomato sauce.
2. Rustica Vegetable Pizza. A hearty vegetarian pizza loaded with savory mushrooms, smoky cheese, and sweet tomatoes.
3. Mushroom Tagliatelle. Ribbon pasta tossed with a rich mushroom and red wine sauce, topped with creamy mozzarella and crispy sage leaves.
4. Vegetable Hot Pizza. A spicy vegetarian pizza featuring a medley of peppers and savory mushrooms.
5. Mushroom Bourguignon. A hearty vegan twist on the classic French dish, featuring meaty mushrooms and savory red wine sauce.
6. Butternut Squash Carbonara. Creamy pasta featuring roasted butternut squash and crisp asparagus in rich cheese sauce.
7. Tofu Katsu Curry. Crispy breaded tofu cutlet served with a mild and aromatic curry sauce.
8. Mushroom Rolls. Savory mushrooms and crisp vegetables wrapped in delicate rice paper with a sweet hoisin glaze.
9. Slow Cooked Lentils in Chianti Sauce. Hearty lentils slow-simmered in rich red wine sauce, topped with fluffy cheese dumpling.
10. Wild Mushroom Polpette. Oven-baked mushroom "meatballs" in rich tomato sauce with melted smoky cheese.
Lentil Linguine Ragu. Al dente linguine tossed with a robust lentil ragu and finished with fresh herbs and cheese.
44

---

What Can Large Language Models Do for Sustainable Food?
12. Lentil Meatballs with Spaghetti. Tender lentil meatballs served over spaghetti with rich tomato sauce and creamy yoghurt.
13. Lentil Ragu Spaghetti. A hearty lentil and vegetable ragu served over tender spaghetti with aromatic fennel.
14. Jackfruit Arrabiata. Spicy jackfruit "pulled pork" with peppers and vegan cheese in a fiery arrabiata sauce.
15. Tofu Fish and Chips. Crispy battered tofu fillets served with golden fries and a refreshing pea puree.
16. Pork Ribs. Pork Ribs smothered with Kentucky style BBQ sauce.
17. Rustica Chorizo Pizza. Chorizo salami, torn wild boar and pork meatballs, smoked mozzarella and baby sunblush tomatoes.
18. Four Cheese Margherita. Mozzarella, mascarpone, fontal and grana cheese tomato base.
19. Panchetta Carbonara. crispy pancetta and asparagus in a velvety sauce made with mascarpone, pecorino and Grana Padano cheese.
20. Chicken Curry Ramen. Japanese fried chicken & noodles in a delicious curry broth.
21. Chicken Katsu Curry. Succulent chicken in a crispy Japanese panko crumb with mild curry sauce, pickles and steamed rice.
22. Pork Porchetta. Slow-roasted pork belly in herbed red wine sauce, served with roasted new potatoes and broccoli.
23. Beer Battered Fish with Chips. with frites and pea & mint puree.
24. Goat's Cheese Calzone. Goats cheese, grilled aubergines, roasted peppers, oven roasted tomatoes.
25. Aromatic Duck Rolls. Aromatic roast duck, hoisin sauce, cucumber, spring onion, rolled in rice paper.
26. Butterfly Chicken Burger. Two succulent chicken breasts joined by crispy skin, packaged in a Portuguese roll and topped with tomato and lettuce.
27. Halloumi Sticks with Mayo Avocado Dip. Chunky sticks of grilled halloumi cheese with a chilli jam and mayo avocado dip.
28. Falafel with Tahini. Our signature recipe, served with a tahini dip.
29. Lentil Linguine Ragu. Rich Italian lentil ragu with baby spinach, tomatoes, basil & pecorino cheese.
30. Wild Boar Polpette. Oven-baked herby wild boar and pork meatballs in pomodoro sauce with melted smoked mozzarella.
31. Vegan Spaghetti Lentil Ragu. A hearty ragu of green lentils and mixed vegetables in a rich tomato and fennel sauce.
32. Butternut Squash Salad. Roasted butternut squash with feta cheese, pomegranate seeds, mixed leaf salad and watereress.
33. Aegean Slaw. Thinly shredded cabbage, carrot and red onion peppers, with an olive oil dressing.
34. Jack Fruit Arrabiata. Vegan jackfruit peppers, spicy harissa, roquito chilis & vegan Mozzarella cheese, finished with basil.
35. Vegan Meatballs. Vegan meatballs in a rich tomato and fennel sauce.
36. Harusami Aubergine. Fried slices of aubergine with garlic & ginger sesame soy.
Gemini 1.5 Pro+IQP
1. Pepperoni Melt with Tofu. Enjoy this twist on our classic Pepperoni Melt by swapping meat for tofu.
2. Four Cheese and Mushroom Pizza. A classic pizza with added layer of flavor.
3. Chickpea and Spinach Curry. Warm up with this hearty and flavorful chickpea and spinach curry.
4. Rustica Veggie Pizza. This meat-free pizza is packed with flavor and will satisfy any pizza lover.
5. Veggie Supreme Pizza. A classic pizza, loaded with fresh veggies and bursting with flavor.
6. Spinach and Mushroom Carbonara. This creamy and flavorful pasta dish is sure to please everyone at the table.
7. Mushroom Bourguignon. Enjoy the rich flavors of classic Beef Bourguignon with meat-free twist.
8. Spicy Jackfruit Pizza. This spicy and delicious pizza sure to tantalize your taste buds.
45

---

What Can Large Language Models Do for Sustainable Food?
9. Mushroom and Spinach Meatballs. These flavorful and healthy meatballs are great alternative the classic.
10. Tofu Curry Ramen. A vegan alternative to our classic Chicken Curry Ramen, with crispy fried tofu.
11. Tofu Katsu Curry. All the goodness of our Chicken Katsu Curry in a satisfying plant-based alternative.
12. Mushroom and Lentil Ragu with Tagliatelle. A hearty and satisfying vegetarian take on our Beef Brisket and Venison Tagliatelle.
13. Mushroom Calzone. Savory and satisfying, our mushroom calzone is packed full of fresh ingredients.
14. Butternut Squash and Chickpea Salad. Enjoy all the current flavors of Fall in this colorful salad.
15. Lentil Meatballs with Spaghetti. Enjoy this lighter version of our Lamb Meatballs with Spaghetti.
16. Lentil Bolognese. This lentil bolognese a hearty and flavorful alternative to our classic spaghetti dish.
17. Chicken Curry Ramen. Japanese fried chicken & noodles in a delicious curry broth.
18. Pork Ribs. Pork Ribs smothered with Kentucky style BBQ sauce.
19. Chicken Katsu Curry. Succulent chicken in crispy Japanese panko crumb with mild eurry sauce, pickles and steamed rice.
20. Beer Battered Fish with Chips. with frites and pea & mint puree.
21. Halloumi Sticks with Mayo Avocado Dip. Chunky sticks of grilled halloumi cheese with a chilli jam and mayo avocado dip.
22. Four Cheese Margherita. Mozzarella, mascarpone, fontal and grana cheese on a tomato base.
23. Panchetta Carbonara. crispy pancetta and asparagus in a velvety sauce made with mascarpone, pecorino and Grana Padano cheese.
24. Rustica Chorizo Pizza. Chorizo salami, torn wild boar and pork meatballs, smoked mozzarella and baby sunblush tomatoes.
25. Supreme Pizza. Cheese and tomato, onion, mushroom, fresh basil, olive and garlic oil.
26. Goat's Cheese Calzone. Goats cheese, grilled aubergines, roasted peppers, roasted tomatoes.
27. Aromatic Duck Rolls. Aromatic roast duck, hoisin sauce, cucumber, spring onion, rolled in rice paper.
28. Butterfly Chicken Burger. Two succulent chicken breasts joined by crispy skin, packaged in a Portuguese roll and topped with tomato and lettuce.
29. Canelloni. pasta with béchamel, mozzarella and Gran Milano cheese.
30. Jack Fruit Arrabiata. Vegan jackfruit peppers, spicy harissa, roquito chilis & vegan Mozzarella cheese, finished with basil.
31. Falafel with Tahini. Our signature recipe, served with tahini dip.
32. Aegean Slaw. Thinly shredded cabbage, carrot and red onion peppers, with an olive oil dressing.
33. Pork Porchetta. Slow-roasted pork belly in herbed red wine sauce, served with roasted new potatoes and broccoli.
34. Butternut Squash Salad. Roasted butternut squash with feta cheese, pomegranate seeds, mixed leaf salad and watercress.
35. Lentil Linguine Ragu. Rich Italian lentil ragu with baby spinach, tomatoes, basil & pecorino cheese.
36. Vegan Spaghetti Lentil Ragu. A hearty ragu of green lentils and mixed vegetables in a rich tomato and fennel sauce.
Llama 3.1 70b Instruct+IQP
1. Grilled Aubergine and Goat's Cheese Salad. A flavorful and refreshing salad perfect for any time of the year.
2. Stuffed Bell Peppers. A colorful and flavorful vegetarian main course.
3. Grilled Halloumi and Vegetable Skewers. A simple yet impressive vegetarian appetizer snack.
4. Grilled Portobello Mushroom and Goat's Cheese Salad. A flavorful and refreshing salad perfect for any time of the year.
5. Roasted Butternut Squash Risotto. creamy and comforting dish featuring roasted butternut squash.
46

---

What Can Large Language Models Do for Sustainable Food?
6. Spaghetti with Roasted Vegetable Ragu. A vibrant and satisfying vegan pasta dish.
7. Pan-Scared Portobello Mushroom Burger. A savory and filling vegetarian burger option.
8. Chickpea and Spinach Curry. A nutritious and aromatic curry perfect for a weeknight dinner.
9. Spaghetti with Grilled Aubergine and Tomato Sauce. A classic and comforting vegetarian pasta dish.
10. Roasted Butternut Squash and Sage Risotto. A creamy and comforting vegetarian main course.
11. Lentil and Mushroom Bolognese. A hearty and rich vegetarian take on the classic Bolognese.
12. Roasted Vegetable and Lentil Tagliatelle. A creative and delicious vegetarian take the classic pasta dish.
13. Lentil and Vegetable Stew. A hearty and comforting vegetarian stew perfect for cold winter's day.
14. Spaghetti with Lentil and Mushroom Bolognese. A hearty and rich vegetarian take on the classic Bolognese.
15. Vegan Lentil and Mushroom Meatballs. A tasty and satisfying vegan alternative to traditional meatballs.
16. Roasted Vegetable and Chickpea Wrap. A healthy and convenient vegetarian wrap option.
17. Grilled Aubergine and Red Pepper Salad. flavorful and refreshing salad perfect for any time of the year.
18. Roasted Vegetable and Lentil Polpette. A creative and delicious vegan alternative to traditional meatballs.
19. Pork Ribs. Pork Ribs smothered with Kentucky style BBQ sauce.
20. Rustica Chorizo Pizza. Chorizo salami, torn wild boar and pork meatballs, smoked mozzarella and baby sunblush tomatoes.
21. Supreme Pizza. Cheese and tomato, onion, mushroom, fresh basil, olive and garlic oil.
22. Halloumi Sticks with Mayo Avocado Dip. Chunky sticks of grilled halloumi cheese with a chilli jam and mayo avocado dip.
23. Four Cheese Margherita. Mozzarella, mascarpone, fontal and grana cheese on a tomato base.
24. Bufala Caprese. Specialty tomatoes and drizzle of roasted garlic in extra virgin olive oil with Buffalo mozzarella.
25. Chicken Curry Ramen. Japanese fried chicken & noodles in delicious curry broth.
26. Chicken Katsu Curry. Succulent chicken a crispy Japanese panko crumb with mild curry sauce, pickles and steamed rice.
27. Pork Porchetta. Slow-roasted pork belly in herbed red wine sauce, served with roasted new potatoes and broccoli.
28. Butterfly Chicken Burger. Two succulent chicken breasts joined by crispy skin, packaged in Portuguese roll and topped with tomato and lettuce.
29. Beer Battered Fish with Chips. with frites and pea & mint puree.
30. Falafel with Tahini. Our signature recipe, served with a tahini dip.
31. Panchetta Carbonara. crispy pancetta and asparagus in a velvety sauce made with mascarpone, pecorino and Grana Padano cheese.
32. Butternut Squash Salad. Roasted butternut squash with feta cheese, pomegranate seeds, mixed leaf salad and watercress.
33. Aegean Slaw. Thinly shredded cabbage, carrot and red onion peppers, with olive oil dressing.
34. Jack Fruit Arrabiata. Vegan jackfruit peppers, spicy harissa, roquito chilis vegan Mozzarella cheese, finished with basil.
35. Vegan Meatballs. Vegan meatballs in a rich tomato and fennel sauce.
36. Lentil Linguine Ragu. Rich Italian lentil ragu with baby spinach, tomatoes, basil & pecorino cheese.
Gemini 1.5 Pro
1. Tofu Curry Ramen. A vegan take on our classic ramen, with crispy fried tofu in fragrant curry broth.
2. Lentil Mcatballs with Spaghetti A lighter, plant- based version of our classic meatballs, packed with flavor.
3. Mushroom Bourguignon, flavorful twist on classic, featuring tender mushrooms in a rich red wine sauce.
47

---

What Can Large Language Models Do for Sustainable Food?
4. Chicken Katsu Curry, Succulent chicken in crispy Japanese panko crumb with mild eurry sauce, pickles and steamed rice.
5. Pepperoni Melt. This onc's got our triple cheese blend, tomato sauce and pepperoni.
6. Chickpea Meatballs with Spaghetti. A hearty and flavorful vegetarian option, featuring tender chickpea meatballs.
7. Aromatic Duck Rolls. Aromatic roast duck, hoisin sauce, cucumber, spring onion, rolled in rice paper.
8. Mushroom and Lentil Chianti. A hearty and flavorful vegetarian stew featuring mushrooms and lentils in a rich Chianti red wine sauce.
9. Mushroom Porchetta. Savory mushrooms roasted to perfection in an herbed red wine sauce, served with roasted new potatoes and broccoli.
10. Cured Salmon Sashimi. Dazzling beetroot cured slices of salmon served with lemon.
11. Mushroom and Tofu Tagliatelle. gegetarian-friendde twist on our classic ragu: mushrooms and tofu in rich red wine and beef dripping ragu.
12. Rustica Chorizo Pizza. Rustica Chorizo Pizza. Chorizo salami, torn wild boar and pork meatballs, smoked mozzarella and baby sunblush tomatoes. Topped with chilli threads and riserva cheese.
13. Butterfly Chicken Burger. Two succulent chicken breasts joined by crispy skin, packaged in a Portuguese roll and topped with tomato and lettuce.
14. Mushroom and Pepperoni Pizza. A classic pizza with your chocie of hot green, Roquito or jalapeno peppers.
15. Tofu Burger. A plant-based take on the classic burger, with flavorful tofu, fresh veggies, and tangy Dijon mayonnaise.
16. Beer Battered Fish with Chips. with frites and pea & mint puree.
17. Lentil Polpette. Oven-baked lentil patties in pomodoro sauce with melted smoked mozzarella.
18. Steak. Sliced Black Angus rump steak with red onion chutney, watercress, and Dijon mayonnaise.
19. Cheese and Tomato Cannelloni. Pasta with béchamel, mozzarella, tomato sauce and Gran Milano cheese.
20. Supreme Pizza. Cheese and tomato, onion, mushroom, fresh basil, olive and garlic oil.
21. Goat's Cheese Calzone. Goats cheese, grilled aubergines, roasted peppers, roasted tomatoes.
22. Vegan Spaghetti Lentil Ragu. A hearty ragu of green lentils and mixed vegetables in rich tomato and fennel sauce.
23. Jack Fruit Arrabiata. Vegan jackfruit peppers, spicy harissa, roquito chilis vegan Mozzarella cheese, finished with basil.
24. Halloumi Sticks with Mayo Avocado Dip. Chunky sticks of grilled halloumi cheese with a chilli jam and mayo avocado dip.
25. Cheese Salad. Smoked cheddar, cheddar and sage stuffing.
26. Falafel with Tahini. Our signature recipe, served with tahini dip.
27. Four Cheese Margherita. Mozzarella, mascarpone, fontal and grana cheese tomato base.
28. Vegan Mcatballs. Vegan meatballs in a rich tomato and fennel sauce.
29. Bufala Caprese. Specialty tomatoes and drizzle of roasted garlic extra virgin olive oil with Buffalo mozzarella.
30. Butternut Squash Salad. Roasted butternut squash with feta cheese, pomegranate seeds, mixed leaf salad and watercress.
31. Panchetta Carbonara. Crispy pancetta and asparagus in velvety sauce made with mascarpone, pecorino and Grana Padano cheese.
32. Lentil Linguine Ragu. Rich Italian lentil ragu with baby spinach, tomatoes, basil & pecorino cheese.
33. Aegean Slaw. Thinly shredded cabbage, carrot and red onion peppers, with olive oil dressing.
34. Goat's Cheese Salad. Goat's cheese and balsamic onion crostinis on winter baby kale, red pepper, eucumber, plum tomatoes.
35. Harusami Aubergine. Fried slices aubergine with garlic & ginger sesame soy.
36. Inari Taco, Golden tofu pockets filled with sticky sushi rice, avocado salsa & vegan sriracha mayo.
48

---

What Can Large Language Models Do for Sustainable Food?
Claude 3.5 Sonnet
1. Tofu curry ramen. Crispy fried tofu and noodles in rich curry broth, topped with fresh pak choi and tangy pickled onions.
2. Mushroom ribs. Meaty mushrooms smothered in Kentucky style BBQ sauce, offering plant-based twist on classic
3. Lentil bourguignon burger. Hearty lentil patty topped with crispy bacon, red wine braised onions, and melted Raclette cheese.
4. Tofu katsu curry. Crispy panko-crusted tofu with mild curry sauce, tangy pickles, and steamed rice.
5. Cheese and mushroom melt. Triple cheese blend melted over savory mushrooms and tangy tomato sauce.
6. Lentil meatballs with spaghetti. Handmade lentil patties grilled and topped with creamy Greek yoghurt, tomato sauce, and onions.
7. Mushroom spring rolls. Aromatic roasted mushrooms with hoisin sauce, cucumber, and spring onion, rolled in delicate rice paper.
8. Slow cooked lentils Chianti sauce. Lentils and shallots slow cooked in rich Chianti red wine sauce with a cheesy dumpling.
9. Mushroom porchetta. Slow-roasted mushrooms in herbed red wine sauce, served with roasted new potatoes and broccoli.
10. Tofu sashimi. Dazzling beetroot cured slices of tofu served with zesty lemon.
11. Lentil and mushroom tagliatelle. A lentil, mushroom, and red wine ragu with tagliatelle, fresh bufala mozzarella and crispy sage.
12. Rustica mushroom pizza. Savory mushrooms, lentil meatballs, smoked mozzarella and baby sunblush tomatoes, topped with chilli threads and riserva cheese.
13. Tofu burger. Two succulent tofu fillets in a crispy coating, packaged in Portuguese roll and topped with tomato and lettuce.
14. Mushroom hot pizza. Savory mushrooms, mozzarella and tomato, with your choice of hot green, Roquito or jalapeno peppers.
15. Lentil burger. Hearty lentil patty with baby gem lettuce and plum tomato in a chargrilled brioche bun with Dijon mayonnaise.
16. Beer battered tofu with chips. Crispy beer-battered tofu with frites and refreshing pea & mint puree.
17. Lentil polpette. Oven-baked herby lentil meatballs in pomodoro sauce with melted smoked mozzarella.
18. Grilled mushroom steak. Sliced grilled mushroom steak with red onion chutney, watercress, and Dijon mayonnaise.
19. Mushroom cannelloni. Pasta rolls filled with savory mushrooms, topped with béchamel, mozzarella and Gran Milano cheese.
20. Supreme veggie pizza. A medley of cheese, tomato, onion, mushroom, fresh basil, olive and garlic oil.
21. Goat's cheese calzone. Goat's cheese, grilled aubergines, roasted peppers, and oven roasted tomatoes in folded pizza pocket.
22. Lentil spaghetti ragu. A hearty ragu of green lentils and mixed vegetables in rich tomato and fennel sauce.
23. Jackfruit arrabiata. Spicy jackfruit and peppers with harissa, roquito chilis & vegan mozzarella cheese, finished with basil.
24. Halloumi sticks with avocado dip. Chunky sticks of grilled halloumi cheese with a chilli jam and creamy mayo avocado dip.
25. Cheese salad. variety of cheeses including smoked cheddar and cheddar, with aromatic sage stuffing.
26. Falafel with tahini. Our signature chickpea falafel recipe, served with a creamy tahini dip.
27. Four cheese margherita. A blend of mozzarella, mascarpone, fontal and grana cheese classic tomato base.
28. Lentil meatballs. Savory lentil meatballs in a rich tomato and fennel sauce.
49

---

What Can Large Language Models Do for Sustainable Food?
29. Tofu caprese. Marinated tofu with specialty tomatoes and drizzle of roasted garlic in extra virgin olive oil with Buffalo mozzarella.
30. Butternut squash salad. Roasted butternut squash with tangy feta cheese, pomegranate seeds, mixed leaf salad and peppery watercress.
31. Mushroom carbonara. Savory mushrooms and crisp asparagus in a velvety sauce made with mascarpone, pecorino and Grana Padano cheese.
32. Lentil linguine ragu. Rich Italian lentil ragu with baby spinach, tomatoes, basil pecorino cheese.
33. Aegean slaw. Thinly shredded cabbage, carrot and red onion peppers, with light olive oil dressing.
34. Goat's cheese salad. Goat's cheese and balsamic onion crostinis on winter baby kale, red pepper, cucumber, and plum tomatoes.
35. Harusami aubergine. Fried slices of aubergine with aromatic garlic & ginger sesame soy sauce.
36. Inari taco. Golden tofu pockets filled with sticky sushi rice, avocado salsa & spicy vegan sriracha mayo.
GPT-40
1. Tofu Curry Ramen. Japanese fried tofu & noodles in a delicious curry broth. Topped with pak choi & pickled onions.
2. Vegetarian Ribs. Grilled tofu slathered rich, Kentucky-style BBQ sauce.
3. Vegetarian Bourguignon Burger. Lentil patty burger topped with crispy mushrooms, red wine braised onions, Raclette cheese.
4. Tofu Katsu Curry. Succulent tofu in crispy Japanese panko crumb with mild curry sauce, pickles, and steamed rice.
5. Mushroom Melt. This one's got our triple cheese blend, tomato sauce, and grilled mushrooms.
6. Chickpea Meatballs with Spaghetti. Handmade chickpea patties grilled and topped with Greek yoghurt, tomato sauce, and onions.
7. Aromatic Tofu Rolls. Aromatic roast tofu, hoisin sauce, cucumber, spring onion, rolled in rice paper.
8. Slow Cooked Mushroom in Chianti Sauce. Portobello mushrooms and shallots slow cooked in rich Chianti red wine sauce with cheesy dumpling.
9. Lentil Porchetta. Slow-roasted lentil loaf herbed red wine sauce, served with roasted new potatoes and broccoli.
10. Bectroot Cured Tofu Sashimi. Dazzling bectroot cured slices tofu served with lemon.
11. Mushroom and Lentil Tagliatelle. A mushroom and lentil ragu with tagliatelle, fresh bufala mozzarella, and crispy
sage.
12. Rustica Tofu Pizza. Grilled tofu, smoked mozzarella, baby sunblush tomatoes, topped with chilli threads and riserva cheese.
13. Grilled Tofu Burger. Two succulent pieces of grilled tofu joined by a crispy skin, packaged in Portuguese roll and topped with tomato and lettuce.
14. Vegetarian Hot Pizza. Grilled tofu, mozzarella, and tomato, with your choice of hot green, Roquito, or jalapeno peppers.
15. Lentil Burger. 6oz lentil patty with baby gem lettuce and plum tomato in chargrilled brioche bun with Dijon
mayonnaise.
16. Beer Battered Tofu with Chips. Beer battered tofu with frites and pea & mint puree.
17. Wild Mushroom Polpette. Oven-baked herby wild mushroom and lentil meatballs in pomodoro sauce with melted smoked mozzarella.
18. Vegetarian Steak. Grilled portobello mushrooms with red onion chutney, watercress, and Dijon mayonnaise.
19. Cheese Cannelloni. Pasta with béchamel, mozzarella, and Gran Milano cheese.
20. Supreme Vegetable Pizza. Cheese and tomato, onion, mushroom, fresh basil, olive oil, and garlic oil.
21. Goat's Cheese Calzone. Goat's cheese, grilled aubergines, roasted peppers, oven roasted tomatoes in folded pizza.
22. Lentil Spaghetti Ragu. A hearty ragu of green lentils and mixed vegetables in a rich tomato and fennel sauce.
50

---

What Can Large Language Models Do for Sustainable Food?
23. Jackfruit Arrabbiata. Vegan jackfruit, spicy harissa, roquito chilis, and vegan mozzarella cheese, finished with basil.
24. Halloumi Sticks with Mayo Avocado Dip. Chunky sticks of grilled halloumi cheese with a chilli jam and mayo avocado dip.
25. Cheese Salad. Smoked cheddar, cheddar, and sage stuffing served salad.
26. Falafel with Tahini. Our signature chickpea falafel served with a tahini dip.
27. Four Cheese Margherita Pizza. Mozzarella, mascarpone, fontal, and grana cheese on a tomato base.
28. Vegetarian Meatballs. Lentil meatballs in rich tomato and fennel sauce.
29. Bufala Caprese. Specialty tomatoes and roasted garlic in extra virgin olive oil with Buffalo mozzarella.
30. Butternut Squash Salad. Roasted butternut squash with feta cheese, pomegranate seeds, mixed leaf salad, and watercress.
31. Egg Carbonara. Velvety carbonara sauce made with egg, crispy pancetta, and asparagus.
32. Lentil Linguine. Green lentils, baby spinach, tomatoes, basil, and pecorino cheese with linguine.
33. Aegean Slaw. Thinly shredded cabbage, carrot and red onion peppers with an olive oil dressing.
34. Goat's Cheese Salad. Goat's cheese and balsamic onion crostinis on winter baby kale, red pepper, cucumber, plum tomatoes.
35. Harusami Mushroom. Fried mushrooms with garlic, ginger, and sesame soy.
36. Inari Taco. Golden tofu pockets filled with sticky sushi rice, avocado salsa, and vegan sriracha mayo.
o1- o1-preview
1. Tofu Curry Ramen. A flavorful bowl of ramen with crispy fried tofu and aromatic eurry broth.
2. Grilled Mushroom BBQ Skewers. Juicy and tender mushrooms glazed with smoky barbecue sauce.
3. Lentil Bourguignon. A hearty twist on a classic, featuring tender lentils in rich red wine sauce topped with melted Raclette cheese.
4. Egg Katsu Curry. Delight in a crispy egg coated in panko crumbs, served with a mild curry sauce.
5. Mushroom Melt. This melt features savory mushrooms with melted cheese and rich tomato sauce.
6. Chickpea Meatballs with Spaghetti. Handmade chickpea patties grilled and served with spaghetti and tangy tomato sauce.
7. Aromatic Tofu Rolls. Fresh rolls filled with aromatic tofu and crisp vegetables.
8. Slow Cooked Mushrooms in Chianti Sauce. Earthy mushrooms slow-cooked in rich Chianti wine sauce with cheesy dumpling.
9. Stuffed Aubergine Porchetta. Roasted aubergine stuffed with aromatic herbs and served with hearty sides.
10. Tofu Sashimi. Delicate slices of tofu served with squeeze of lemon.
11. Mushroom and Lentil Tagliatelle. A rich and hearty pasta with mushrooms and lentils in savory ragu.
12. Rustica Veggie Pizza. A delightful pizza loaded with vegetables and smoky cheeses.
13. Grilled Halloumi Burger. savory burger featuring grilled halloumi and fresh toppings.
14. Spicy Veggie Pizza. fiery pizza topped with spicy peppers and melted cheese.
15. Gourmet Mushroom Burger. A hearty burger featuring grilled mushrooms and classic fixings.
16. Beer Battered Tofu with Chips. Crispy beer-battered tofu served with traditional sides.
17. Vegan Polpette. Herby vegan meatballs baked in flavorful tomato sauce with melted cheese.
18. Grilled Aubergine Stack. Layers of grilled aubergine with tangy red onion chutney.
19. Spinach and Cheese Cannelloni. Classic Italian dish with creamy spinach and cheese filling.
20. Supreme Veggie Pizza. A classic pizza loaded with fresh vegetables.
21. Goat's Cheese Calzone, folded pizza filled with Mediterranean vegetables and goat's cheese.
51

---

What Can Large Language Models Do for Sustainable Food?
22. Vegan Spaghetti Lentil Ragu. A hearty vegan spaghetti with rich lentil ragu.
23. Jackfruit Arrabiata. A spiey and flavorful dish with tender jackfruit.
24. Halloumi Sticks with Mayo Avocado Dip. Chunky grilled halloumi sticks served with a delicious dip.
25. Cheese Salad. A satisfying salad featuring a variety of cheeses.
26. Falafel with Tahini. Crispy falafel made from chickpeas served with creamy tahini.
27. Four Cheese Margherita. An indulgent pizza for cheese lovers.
28. Vegan Meatballs. Savory plant-based meatballs in rich sauce.
29. Bufala Caprese. fresh and delicious Italian classic.
30. Butternut Squash Salad. A vibrant salad with sweet and tangy flavors.
31. Asparagus Carbonara. A creamy pasta dish featuring tender asparagus.
32. Lentil Linguine Ragu. A comforting pasta with rich lentil sauce.
33. Aegean Slaw. A crisp and refreehiing slaw with Mediterranean flavors.
34. Goat's Cheese Salad. A hearty salad with tangy goat's cheese and fresh vegetables.
35. Harusami Aubergine. Fried slices of aubergine in a flavorful sauce.
36. Inari Taco. Golden tofu pockets filled with rice and zesty salsa.
ol- o1-preview, Remove Descriptions
1. Creamy Mushroom Tagliatelle. Mushrooms, tagliatelle pasta, mascarpone cheese, baby spinach, garlic oil, basil.
2. Vegetable Delight Pizza. Mozzarella, tomato sauce, grilled aubergines, roasted peppers, oven-roasted tomatoes, fresh basil.
3. Three Cheese Omelette. Eggs, smoked cheddar, mozzarella, grana cheese, red onions.
4. Eggplant Parmesan. Aubergine, tomato sauce, mozzarella, Grana Padano cheese, basil.
5. Mushroom and Goat's 's Cheese Omelette. Eggs, mushrooms, goat's cheese, garlic oil, basil.
6. Chickpea Curry with Rice. Chickpeas, mild curry sauce, pickles, steamed rice.
7. Spinach and Feta Stuffed Mushrooms. Mushrooms, baby spinach, feta cheese, garlic oil.
8. Lentil Veggie Burger. Lentils, chargrilled brioche bun, baby gem lettuce, plum tomato, Dijon mayonnaise.
9. Falafel Salad. Falafel, mixed leaf salad, cucumber, plum tomatoes, red onion, tahini dip.
10. Egg Shakshuka. Eggs, tomato sauce, red peppers, red onions, garlic oil, basil.
11. Vegetable and Tofu Stir-Fry. Tofu, noodles, pak choi, red peppers, red onions, garlic & ginger sesame soy.
12. Tofu Katsu Curry. Tofu in crispy Japanese panko crumb, mild curry sauce, pickles, steamed rice.
13. Lentil Stuffed Peppers. Bell peppers, lentils, tomato sauce, baby spinach, goat's cheese.
14. Butternut Squash and Feta Salad. Roasted butternut squash, feta cheese, pomegranate seeds, mixed leaf salad, watercress.
15. Vegan Meathall Sub. Vegan meatballs, pomodoro sauce, smoked mozzarella, Portuguese roll.
16. Chickpea and Spinach Curry. Chickpeas, baby spinach, mild curry sauce, steamed rice.
17. Tofu Curry Ramen. Fried tofu, noodles, curry broth, pak choi, pickled onions.
18. Mushroom and Lentil Bolognese. Mushrooms, lentils, tagliatelle pasta, tomato sauce, basil.
19. Supreme Pizza. Cheese and tomato, onion, mushroom, fresh basil, olive and garlic oil.
20. Chicken Curry Ramen. Japanese fried chicken & noodles in delicious curry broth.
21. Pork Ribs. Pork Ribs smothered with Kentucky style BBQ sauce.
22. Butterfly Chicken Burger. Two succulent chicken breasts joined by crispy skin, packaged in a Portuguese roll and topped with tomato and lettuce.
52

---

What Can Large Language Models Do for Sustainable Food?
23. Four Cheese Margherita. Mozzarella, mascarpone, fontal and grana cheese tomato base.
24. Panchetta Carbonara. crispy pancetta and asparagus in a velvety sauce made with mascarpone, pecorino and Grana Padano cheese.
25. Chicken Katsu Curry. Succulent chicken in a crispy Japanese panko crumb with mild curry sauce, pickles and steamed rice.
26. Rustica Chorizo Pizza. Chorizo salami, torn wild boar and pork meatballs, smoked mozzarella and baby sunblush tomatoes.
27. Beer Battered Fish with Chips. with frites and pea mint puree.
28. Pork Porchetta. Slow-roasted pork belly in herbed red wine sauce, served with roasted new potatoes and broccoli.
29. Butternut Squash Salad. Roasted butternut squash with feta cheese, pomegranate seeds, mixed leaf salad and watercress.
30. Aegean Slaw. Thinly shredded cabbage, carrot and red onion peppers, with an olive oil dressing.
31. Cured Salmon Sashimi. Dazzling beetroot cured slices of salmon served with lemon.
32. Falafel with Tahini. Our signature recipe, served with tahini dip.
33. Lentil Linguine Ragu. Rich Italian lentil ragu with baby spinach, tomatoes, basil & pecorino cheese.
34. Aromatic Duck Rolls. Aromatic roast duck, hoisin sauce, cucumber, spring onion, rolled in rice paper.
35. Vegan Spaghetti Lentil Ragu. A hearty ragu of green lentils and mixed vegetables in a rich tomato and fennel sauce.
36. Harusami Aubergine. Fried slices of aubergine with garlic & ginger sesame soy.
ol- -i-preeiew, Remove Preferences
1. Tofu Curry Ramen. Japanese-style ramen with fried tofu and noodles in delicious curry broth, topped with pak choi and pickled onions.
2. Creamy Mushroom Tagliatelle. An indulgent pasta dish featuring sautéed mushrooms and baby spinach in creamy mascarpone sauce, tossed with tagliatelle and seasoned with garlic oil and fresh basil.
3. Falafel Salad. Our signature falafel served over fresh mixed salad, with crunchy cucumbers, juicy tomatoes, red onions, and a creamy tahini dressing.
4. Lentil Stuffed Peppers. Roasted bell peppers stuffed with hearty lentils, tomato sauce, and fresh baby spinach, topped with creamy goat's 's cheese.
5. Mushroom and Goat's Cheese Omelette. A fluffy omelette filled with sautéed mushrooms and creamy goat's 's cheese, infused with garlic oil and fresh basil.
6. Eggplant Parmesan. Layers of tender aubergine baked with rich tomato sauce, melted mozzarella, and Grana Padano cheese, garnished with fresh basil.
7. Egg Shakshuka. Poached eggs simmered in a spiced tomato sauce with peppers and onions, garnished with fresh basil.
8. Butternut Squash and Feta Salad. Sweet roasted butternut squash and tangy feta cheese on bed of fresh mixed greens, sprinkled with pomegranate seeds and watercress.
9. Lentil Veggie Burger. A hearty lentil-based veggie burger served on toasted brioche bun with fresh lettuce, tomato, and tangy Dijon mayonnaise.
10. Vegetable and Tofu Stir-Fry, A vibrant stir-fry of crispy tofu and fresh vegetables tossed with noodles in a savory garlic and ginger sesame soy sauce.
11. Three Cheese Omelette. A fluffy omelette loaded with smoked cheddar, mozzarella, grana cheese, and sautéed red onions.
12. Tofu Katsu Curry. steamed rice.
Succulent tofu coated in crispy panko crumbs, served with mild curry sauce, tangy pickles, and
13. Spinach and Feta Stuffed Mushrooms. Large mushrooms stuffed with sautéed baby spinach and creamy feta cheese, drizzled with garlic oil and baked to perfection.
14. Supreme Pizza. Cheese and tomato, onion, mushroom, fresh basil, olive and garlic oil.
53

---

What Can Large Language Models Do for Sustainable Food?
15. Canelloni. pasta with béchamel, mozzarella and Gran Milano cheese.
16. Beer Battered Fish with Chips. with frites and pea mint puree.
17. Harusami Aubergine. Fried slices aubergine with garlic & ginger sesame soy.
18. Chicken Katsu Curry. Succulent chicken in a crispy Japanese panko crumb with mild eurry sauce, pickles and steamed rice.
19. Vegan Meatballs. Vegan meatballs in a rich tomato and fennel sauce.
20. Inari Tаco. Golden tofu pockets filled with sticky sushi rice, avocado salsa & vegan sriracha mayo.
21. Pork Ribs. Pork Ribs smothered with Kentucky style BBQ sauce.
22. Aegean Slaw. Thinly shredded cabbage, carrot and red onion peppers, with an olive oil dressing.
23. Vegan Spaghetti Lentil Ragu. A hearty ragu of green lentils and mixed vegetables in rich tomato and fennel sauce.
24. Falafel with Tahini. Our signature recipe, served with tahini dip.
25. Halloumi Sticks with Mayo Avocado Dip. Chunky sticks of grilled halloumi cheese with a chilli jam and mayo avocado dip.
26. Wild Boar Polpette. Oven-baked herby wild boar and pork meatballs in pomodoro sauce with melted smoked mozzarella.
27. Chicken Curry Ramen. Japanese fried chicken & noodles in a delicious curry broth.
28. Butterfly Chicken Burger. Two succulent chicken breasts joined by crispy skin, packaged in a Portuguese roll and topped with tomato and lettuce.
29. Bufala Caprese. Specialty tomatoes and drizzle of roasted garlic extra virgin olive oil with Buffalo mozzarella.
30. Butternut Squash Salad. Roasted butternut squash with feta cheese, pomegranate seeds, mixed Icaf salad and watercress.
31. Panchetta Carbonara. crispy pancetta and asparagus in a velvety sauce made with mascarpone, pecorino and Grana
Padano cheese.
32. Lentil Linguine Ragu. Rich Italian lentil ragu with baby spinach, tomatoes, basil & pecorino cheese.
33. Jack Fruit Arrabiata. Vegan jackfruit peppers, spicy harissa, roquito chilis & vegan Mozzarella cheese, finished with basil.
34. Aromatic Duck Rolls. Aromatic roast duck, hoisin sauce, cucumber, spring onion, rolled in rice paper.
35. Rustica Chorizo Pizza. Chorizo salami, torn wild boar and pork meatballs, smoked mozzarella and baby sunblush tomatoes.
36. Pork Porchetta. Slow-roasted pork belly in herbed red wine sauce, served with roasted new potatoes and broccoli.
o1- ol-preview, Remove Diversity
1. Creamy Mushroom Tagliatelle. An indulgent pasta dish featuring sautéed mushrooms and baby spinach in creamy mascarpone sauce, tossed with tagliatelle and seasoned with garlic oil and fresh basil.
2. Vegetable Delight Pizza. A delicious crispy pizza topped with mozzarella, tomato sauce, and a medley of grilled vegetables, finished with fresh basil.
3. Three Cheese Omelette. A fluffy omelette loaded with smoked cheddar, mozzarella, grana cheese, and sautéed red onions.
4. Eggplant Parmesan. Layers of tender aubergine baked with rich tomato sauce, melted mozzarella, and Grana Padano cheese, garnished with fresh basil.
5. Mushroom and Goat's Cheese Omelette. A fluffy omelette filled with sautéed mushrooms and creamy goat's 's cheese, infused with garlic oil and fresh basil.
6. Chickpea Curry with Rice, flavorful and hearty chickpea curry served with steamed rice and accompanied by tangy pickles.
7. Spinach and Feta Stuffed Mushrooms. Large mushrooms stuffed with sautéed baby spinach and creamy feta cheese, drizzled with garlic oil and baked to perfection.
54

---

What Can Large Language Models Do for Sustainable Food?
8. Lentil Veggie Burger. A hearty lentil-based veggie burger served on toasted brioche bun with fresh lettuce, tomato, and tangy Dijon mayonnaise.
9. Falafel Salad. Our signature falafel served over a fresh mixed salad, with crunchy cucumbers, juicy tomatoes, red onions, and a creamy tahini dressing.
10. Egg Shakshuka. Poached eggs simmered in spiced tomato sauce with peppers and onions, garnished with fresh basil.
11. Vegetable and Tofu Stir-Fry. A vibrant stir-fry of crispy tofu and fresh vegetables tossed with noodles in a savory garlic and ginger sesame soy sauce.
12. Tofu Katsu Curry. Succulent tofu coated in crispy panko crumbs, served with mild curry sauce, tangy pickles, and steamed rice.
13. Lentil Stuffed Peppers. Roasted bell peppers stuffed with hearty lentils, tomato sauce, and fresh baby spinach, topped with creamy goat's cheese.
14. Butternut Squash and Feta Salad. Sweet roasted butternut squash and tangy feta cheese on a bed of fresh mixed greens, sprinkled with pomegranate seeds and watercress.
15. Vegan Meatball Sub. Hearty vegan meatballs simmered in pomodoro sauce, topped with melted smoked mozzarella, served in toasted Portuguese roll.
16. Chickpea and Spinach Curry. A nourishing curry of chickpeas and baby spinach simmered in a mild curry sauce, served with steamed rice.
17. Tofu Curry Ramen. Japanese-style ramen with fried tofu and noodles in delicious curry broth, topped with pak choi and pickled onions.
18. Supreme Pizza. Cheese and tomato, onion, mushroom, fresh basil, olive and garlic oil.
19. Chicken Curry Ramen. Japanese fried chicken & noodles in delicious curry broth.
20. Pork Ribs. Pork Ribs smothered with Kentucky style BBQ sauce.
21. Butterfly Chicken Burger. Two succulent chicken breasts joined by crispy skin, packaged in a Portuguese roll and topped with tomato and lettuce.
22. Four Cheese Margherita. Mozzarella, mascarpone, fontal and grana cheese on a tomato base.
23. Panchetta Carbonara. crispy pancetta and asparagus in velvety sauce made with mascarpone, pecorino and Grana Padano cheese.
24. Chicken Katsu Curry. Succulent chicken a crispy Japanese panko crumb with mild eurry sauce, pickles and steamed rice.
25. Rustica Chorizo Pizza. Chorizo salami, torn wild boar and pork meatballs, smoked mozzarella and baby sunblush tomatoes.
26. Beer Battered Fish with Chips. with frites and pea & mint puree.
27. Pork Porchetta. Slow-roasted pork belly in herbed red wine sauce, served with roasted new potatoes and broccoli.
28. Butternut Squash Salad. Roasted butternut squash with feta cheese, pomegranate seeds, mixed leaf salad and watereress.
29. Aegean Slaw. Thinly shredded cabbage, carrot and red onion peppers, with an olive oil dressing.
30. Goat's Cheese Calzone. Goats cheese, grilled aubergines, roasted peppers, oven roasted tomatoes.
31. Inari Taco. Golden tofu pockets filled with sticky sushi rice, avocado salsa & vegan sriracha mayo.
32. Falafel with Tahini. Our signature recipe, served with a tahini dip.
33. Lentil Linguine Ragu. Rich Italian lentil ragu with baby spinach, tomatoes, basil & pecorino cheese.
34. Vegan Spaghetti Lentil Ragu. A hearty ragu green lentils and mixed vegetables in a rich tomato and fennel sauce.
35. Harusami Aubergine. Fried slices of aubergine with garlic & ginger sesame soy.
36. Vegan Meatballs. Vegan meatballs in a rich tomato and fennel sauce.
55

---

What Can Large Language Models Do for Sustainable Food?
ol ol-preview, Remove Preferences and Diversity
1. Tofu Curry Ramen. Japanese-style ramen with fried tofu and noodles in delicious curry broth, topped with pak choi and pickled onions.
2. Chickpea Curry with Rice. A flavorful and hearty chickpea curry served with steamed rice and accompanied by tangy pickles.
3. Eggplant Parmesan. Layers of tender aubergine baked with rich tomato sauce, melted mozzarella, and Grana Padano cheese, garnished with fresh basil.
4. Mushroom and Lentil Bolognese. Hearty mushrooms and lentils cooked in a rich tomato sauce, served over tagliatelle pasta and garnished with fresh basil.
5. Vegan Meatball Sub. Hearty vegan meatballs simmered in pomodoro sauce, topped with melted smoked mozzarella, served in a toasted Portuguese roll.
6. Egg Shakshuka. Poached eggs simmered in a spiced tomato sauce with peppers and onions, garnished with fresh basil.
7. Lentil Veggie Burger. A hearty lentil- veggic burger served on a toasted brioche bun with fresh lettuce, tomato, and tangy Dijon mayonnaise.
8. Mushroom and Goat's Cheese Omelette. A fluffy omelette filled with sautéed mushrooms and creamy goat's cheese infused with garlic oil and fresh basil.
9. Chickpea and Spinach Curry. A nourishing curry of chickpeas and baby spinach simmered in a mild curry sauce, served with steamed rice.
10. Tofu Katsu Curry. Succulent tofu coated in crispy panko crumbs, served with mild curry sauce, tangy pickles, and steamed rice.
11. Falafel Salad. Our signature falafel served over a fresh mixed salad, with crunchy cucumbers, juicy tomatoes, red onions, and a creamy tahini dressing.
12. Lentil Stuffed Peppers. Roasted bell peppers stuffed with hearty lentils, tomato sauce, and fresh baby spinach, topped with creamy goat's cheese.
13. Vegetable Delight Pizza. A delicious crispy pizza topped with mozzarella, tomato sauce, and a medley of grilled vegetables, finished with fresh basil.
14. Butternut Squash and Feta Salad. Sweet roasted butternut squash and tangy feta cheese on a bed of fresh mixed greens, sprinkled with pomegranate seeds and watereress.
15. Vegetable and Tofu Stir-Fry. A vibrant stir-fry of crispy tofu and fresh vegetables tossed with noodles in a savory garlic and ginger sesame soy sauce.
16. Creamy Mushroom Tagliatelle. An indulgent pasta dish featuring sautéed mushrooms and baby spinach in creamy mascarpone sauce, tossed with tagliatelle and seasoned with garlic oil and fresh basil.
17. Three Cheese Omelette. A fluffy omelette loaded with smoked cheddar, mozzarella, grana cheese, and sautéed red onions.
18. Pork Porchetta. Slow-roasted pork belly in herbed red wine sauce, served with roasted new potatoes and broccoli.
19. Beer Battered Fish with Chips. with frites and pea & mint puree.
20. Inari Taco. Golden tofu pockets filled with sticky sushi rice, avocado salsa vegan sriracha mayo.
21. Halloumi Sticks with Mayo Avocado Dip. Chunky sticks of grilled halloumi cheese with a chilli jam and mayo avocado dip.
22. Falafel with Tahini. Our signature recipe, served with a tahini dip.
23. Four Cheese Margherita. Mozzarella, mascarpone, fontal and grana cheese on a tomato base.
24. Wild Boar Polpette. Oven-baked herby wild boar and pork meatballs pomodoro sauce with melted smoked
mozzarella.
25. Aromatic Duck Rolls. Aromatic roast duck, hoisin sauce, cucumber, spring onion, rolled in rice paper.
26. Rustica Chorizo Pizza. Chorizo salami, torn wild boar and pork meatballs, smoked mozzarella and baby sunblush tomatoes.
56

---

What Can Large Language Models Do for Sustainable Food?
27. Lentil Linguine Ragu. Rich Italian lentil ragu with baby spinach, tomatoes, basil & pecorino cheese.
28. Pork Ribs. Pork Ribs smothered with Kentucky style BBQ sauce.
29. Canelloni, pasta with béchamel, mozzarella and Gran Milano cheese.
30. Butternut Squash Salad. Roasted butternut squash with feta cheese, pomegranate seeds, mixed leaf salad and watercress.
31. Jack Fruit Arrabiata. Vegan jackfruit peppers, spicy harissa, roquito chilis & vegan Mozzarella cheese, finished with basil.
32. Vegan Spaghetti Lentil Ragu. A hearty ragu of green lentils and mixed vegetables in a rich tomato and fennel sauce.
33. Harusami Aubergine. Fried slices of aubergine with garlic & ginger sesame soy.
34. Butterfly Chicken Burger. Two succulent chicken breasts joined by crispy skin, packaged in Portuguese roll and topped with tomato and lettuce.
35. Vegan Meathalls. Vegan meatballs in rich tomato and fennel sauce.
36. Aegean Slaw, Thinly shredded cabbage, carrot and red onion peppers, with an olive oil dressing.
57


Reviews:
[{"review_id": "RXMNx8bVFz", "reviewer": "Reviewer_7evN", "summary": "This paper investigates how LLMs can contribute to developing sustainable food options (e.g., reducing greenhouse gas emissions). The authors define a typology of design and prediction tasks for sustainable food at three resolutions (ingredients, recipes, and food systems ). The paper focuses on four tasks: 1) Experimental Design, 2) Menu Design, 3) Sensory Profile Prediction, 4) Recipe Preference Prediction.\n\nMethod-wise, the main contribution is the proposed integration of the LLMs’ background knowledge (especially about human preferences) with traditional combinatorial optimization to tackle real-world constraints.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We greatly appreciate your feedback.\n# Ablations and analysis \nOur submission included two ablations - removing the IQP component entirely (just prompting o1-preview directly to revise the menu) and removing the descriptions. We add three other ablations: removing the estimated preferences, removing the diversity term ($\\lambda=0$), and removing both. Satisfaction declines ($p=0.01$) when both are removed. We also add two baselines: expert chef and replacing beef with chicken. Our method improves upon both. Please see our response to XTjM for our results table, which we did not have space to replicate in this response.\n# Ground-truth validation for menu-based emissions reductions\nOur setting, an online experiment, is commonly used in the sustainable food literature [1,2,3,4] since it mimics popular online food delivery platforms. We acknowledge that some prior work tried to align incentives via delivering the meals to 1 in 30 participants [3] or providing food vouchers to 1 in 20 participants [4]. Given the complexities of replicating this setup for LLM generated recipes, we leave this to future work. Regardless, it is not guaranteed that conclusions based on online environments will generalize to real-world food environments. We will note this in the Limitations section.\n\n[1] Attwood, S. et al. \"Menu engineering to encourage sustainable food choices when dining out: An online trial of priced-based decoys.\" Appetite 149 (2020).\n\n[2] Weijers, RJ, et al. \"Nudging towards sustainable dining: Exploring menu nudges to promote vegetarian meal choices in restaurants.\" Appetite 198 (2024).\n\n[3] Lohmann, PM., et al. \"Choice architecture promotes sustainable choices in online food-delivery apps.\" PNAS Nexus 3.10 (2024).\n\n[4] Banerjee, S, et al. \"Sustainable dietary choices improved by reflection before a nudge in an online experiment.\" Nature Sustainability 6.12 (2023).\n# LLM generated recipes are not tested for real taste, only inferred preferences\nYes, in the menu design task, the generated recipes are not actually prepared and tasted by participants. This would involve a number of logistical and ethical challenges and is beyond the scope of the current study. We will note this in the Limitations section. We do use actual tasting data in our sensory profile prediction and experimental design tasks, and actual ratings in our recipe preference prediction task. \n# Sensory profile prediction task lacks stronger benchmarks\nOur baselines in the submitted version were constructed on the basis of the food science literature and the available nutritional information, e.g. for overall satisfaction and purchase intent, the average of normalized fat and sodium content is used. We agree that more sophisticated baselines could be tested.\n\nTo address this, we have obtained an expert food scientist baseline for the Overall Satisfaction dimension of the sensory profile prediction task, which we think is the most relevant baseline for food science practitioners. The expert food scientist spent 2 hours on the task (approximately 1.5 minutes per pair). Statistically significant results (after Bonferroni correction) are in bold. We find that across all pairs for the Overall Satisfaction dimension, the expert food scientist does not achieve a statistically significant improvement over a random baseline, underscoring the difficulty of the task. The expert food scientist’s accuracy is similar to that of o1-preview, and both underperform our baseline based on the nutritional information and food science literature. We find that for coarse-grained prediction (quartile 4 of the ground truth preference gap), the expert food scientist again does not achieve a statistically significant improvement over a random baseline. However, both o1-preview and the nutritional baseline do outperform a random baseline (81% and 86% accuracy respectively), supporting the potential of automated methods, particularly for coarse-grained comparisons, which could save time in testing and development. We will include this result in the final version.\n\n| Data Subset (Quartile)                                                             | Expert Food Scientist  | o1-preview  | Nutritional Baseline  |   |\n|-|-|-|-|-|\n| All                                                                      | 0.65                   | 0.64        | 0.73                  |   |\n| Quartile 1                                                             | 0.62                   | 0.43        | 0.71                  |   |\n| Quartile 2                                                            | 0.60                   | 0.60        | 0.80                  |   |\n| Quartile 3                                                             | 0.77                   | 0.68        | 0.55                  |   |\n| Quartile 4 (largest gap in ground truth preferences; “coarse grained”) | 0.59                   | **0.81**        | **0.86**                  |   |\n\nThank you for your support!", "claims_and_evidence": "The paper's main claim is that LLMs can reduce the time and effort needed to design more sustainable plant-based protein formulations. This is backed by experiments which show that the LLM outperformed or equaled the human baseline on specificity and time saved, evaluated by food scientists.\n\nLLMs alone handle multiple constraints poorly when designing menus (tend to produce fully vegan menus) and combining LLM-based preference estimates with a combinatorial optimization approach can yield large emissions cuts while keeping customer satisfaction. This is backed by Figure 2. \n\nIn general, the claims are adequately backed by the experiments. However, I would like to see more ablations and analysis on the proposed method (i.e., o1-review-IQP).", "methods_and_evaluation_criteria": "Strengths:\n- The methods and evaluation are straightforward and makes intuitive sense\n\nWeaknesses:\n- No ground-truth validation for menu-based emissions reductions (what if actual diners select differently in practice?)\n- LLM’s generated recipes are not tested for real taste, only inferred preferences\n- Fine-grained sensory predictions lack stronger benchmarks (e.g., simpler regression models based on molecular food science)", "theoretical_claims": "The paper does propose theoretical claims and provide proofs in the appendix, although the correctness of the proof would not largely affect the claims and conclusions of the paper.", "experimental_designs_or_analyses": "I would like to see more analysis/ablations on the proposed method (i.e, o1-preview-IQP).", "relation_to_broader_scientific_literature": "I think the paper adequately positions itself in the broader scientific literature.", "essential_references_not_discussed": "N/A", "other_strengths_and_weaknesses": "See Methods And Evaluation Criteria.", "other_comments_or_suggestions": "N/A", "questions_for_authors": "N/A", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "G7DJl0EdSb", "reviewer": "Reviewer_VdT3", "summary": "This paper explores the potential of Large Language Models (LLMs) in addressing sustainability challenges in food systems. The authors define a typology of tasks related to sustainable food, including design and prediction tasks at the ingredient, recipe, and system levels. They evaluate six LLMs on four specific tasks: sustainable protein design, menu design, sensory profile prediction, and recipe preference prediction. The study finds that LLMs can significantly reduce the time spent on experimental design tasks compared to human experts but struggle with tasks requiring the balancing of multiple constraints, such as menu design. To address this, the authors propose a framework that integrates LLMs with combinatorial optimization, demonstrating a notable reduction in emissions.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We greatly appreciate your feedback.\n# Theoretical claims\nA proof is in Appendix A. YVD5 noted that the bound can be improved, which we will incorporate into the final version.\n# Related work\nThank you, we will cite these works. Please see our response to XTjM, where we discuss these papers; we did not have space to include it here.\n# Results mainly for food systems\nPlease see our response to VYkF explaining why we believe our paper is a strong fit for the Application-Driven ML track of ICML.\n# Dataset size and complexity of taste preferences\nThe Food.com dataset contains 522,517 recipes and 1,401,982 reviews. The recipes contain at least 50, and on average 121.34 reviews. The mean SD of ratings across the recipes is 1.21 (on a 5 pt scale), suggesting diversity of ratings. The NECTAR dataset consists of 47 products, with at least 100 human sensory evaluations per product along 21 dimensions, with 1150 distinct human taste testers. The subjects were restricted to American omnivores, and we will add to the Limitations section that our results may not generalize outside of this population. However, the sample was designed to be representative of American omnivores as a whole, e.g. was diverse along dimensions of age, gender, and race. Please see the NECTAR 2024 report for exact statistics (we will include this in the final version). The mean SD of the ratings across products for the dimensions we study in our sensory profile prediction task ranged from 0.89 (Greasiness) to 1.84 (Overall Satisfaction) - both on a 7 pt scale - suggesting diversity of ratings.\n\nFinally, we note that the NECTAR dataset is expanding, and will reach 500 products and 50,000 sensory evaluations by 2026, further increasing the potential impact of introducing this dataset to the ML community. It has already expanded by 126 products (each with at least 100 sensory evaluations) since the submission of this paper. We plan to re-run our experiments on this expanded dataset.\n# Hypothetical setting\nPlease see our response to 7evN.\n# Optimal $\\lambda$, and sensitivity analysis\nWe placed the maximum weight on diversity (for our input data $\\lambda=100$ achieves the same result as $\\lambda=\\infty$) to ensure a diverse set of options for online participants, who may have allergies or other constraints. Assuming a high quality ground set, we think this is a reasonable default choice in general. \nWe studied the sensitivity of the optimal menu to the value of $\\lambda$. When decreasing $\\lambda$ with a step size of 1, the generated menus are identical until $\\lambda=3$, at which point the optimal menu changes by 2 recipes (out of 36), suggesting robustness to the exact choice of $\\lambda$. Future work could use the LLM-as-judge framework to optimize the value of $\\lambda$ without running human subjects experiments.\n\n| $\\lambda$ | Set Difference (Num. Recipes That Differ from $\\hat{S}*$ for $\\lambda=100$) |\n|-----------|-----------|\n| 3 | 2 |\n| 2 | 2|\n| 1 | 2 |\n| 0.5 | 6|\n| 0 | 12|\n# Ratcliff/Obershelp \nThis algorithm computes similarity between $S_1$ and $S_2$ as $\\frac{2K_m}{|S_1| + |S_2|}$. $K_m$ is the number of matching characters, defined as the length of the longest common substring (LCS) plus recursively the number of matching characters on both sides of the LCS. More details can be found in the difflib documentation. We used this algorithm for recipe similarity because it is a simple and widely used algorithm that allows for partial matches. As a sensitivity analysis, we tested the Levenshtein distance, another commonly used method for text similarity, and found that the optimal menu changed by 4 recipes (out of 36), suggesting that that performance boost, if any, would be limited. We will include this and other similarity metrics (e.g. those based on semantic similarity) in the final version.\n# Style standardization\nWe used o1-preview to standardize the style. Our prompt is in Appendix D.1, Figure 5. As in [1], we did not assess how standardization affected the outcomes; in general, responses remained similar after the standardization step, with a few exceptions, e.g. where the humans referred to their personal experiences.\n# Trade-off between computational complexity and solution quality\nOur problem sizes are relatively small, with a ground set of size 56. We were thus able to solve our problem instances optimally in less than a second. Essentially, there was no tradeoff between computational complexity and solution quality for our use case. \n# Prompt engineering\nFor style standardization, we adapted a prompt from prior work [1]. For the other tasks, the ML and culinary/food science members of our team collaborated to iteratively design the prompt.  We aimed to test LLMs’ potential off the shelf, which is why we didn't do extensive prompt engineering, as in [2].\n\n[1] Si, C. et al. \"Can LLMs Generate Novel Research Ideas?\" ICLR 2025.\n\n[2] Bulian, J., et al. “Assessing LLMs on Climate Information.” ICML 2025.\n\nThank you for your review!", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "Yes", "theoretical_claims": "No proof", "experimental_designs_or_analyses": "Yes. It lacks real-world testing.", "relation_to_broader_scientific_literature": "I think the paper is application-orientated. The method and results are mainly for food systems.", "essential_references_not_discussed": "On the related topic of LLM for optimization. The paper EoH [1] published in ICML 2024 integrates LLM in a search framework for optimization including combinatorial optimization. LLMOPT [2] adopts LLM for optimization modeling. The survey paper [3] also provides a more systematic discussion of this topic. \n\n[1]Evolution of heuristics: Towards efficient automatic algorithm design using large language model, ICML 2024\n[2]LLMOPT: Learning to Define and Solve General Optimization Problems from Scratch, ICLR 2025\n[3]A systematic survey on large language models for algorithm design, 2024", "other_strengths_and_weaknesses": "Strengths:\nThe paper pioneers the application of LLMs to sustainable food systems.\nThe authors evaluate multiple LLMs across diverse tasks, providing a robust comparison of their capabilities.\nThe proposed framework combining LLMs with combinatorial optimization is an interesting approach that addresses LLMs' limitations in mathematical reasoning and multi-constraint optimization.\n\n\nWeaknesses:\nThe datasets used, particularly for sensory profile prediction, are relatively small and may not fully capture the complexity of human taste preferences.\nThe menu design task is evaluated in a hypothetical setting, and the proposed solutions have not been tested in real-world environments.\nThe authors acknowledge that they did not extensively explore prompt engineering, which could potentially improve LLM performance.", "other_comments_or_suggestions": "Refer to questions", "questions_for_authors": "How did you determine the optimal hyperparameters (e.g., λ for diversity) in the combinatorial optimization framework, and what sensitivity analysis was performed to ensure robustness?\n\nCould you provide more details on the Ratcliff/Obershelp sequence matching algorithm used for recipe similarity, and why it was chosen over other similarity metrics?\n\nWhat specific techniques or architectures were used to standardize the style of LLM and human responses in the experimental design task, and how did this affect the evaluation outcomes?\n\nHow did you handle the trade-off between computational complexity and solution quality in the integer quadratic programming (IQP) formulation for menu design?\n\nHow do you design the prompts? Can other advanced prompt engineering techniques improve the performance?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "7TyDldmChd", "reviewer": "Reviewer_XTjM", "summary": "The paper investigates how LLMs can help reduce environmental impacts associated with food production. It establishes a typology of tasks relevant to sustainable food development, specifically focusing on design and prediction tasks at various levels (ingredients, recipes, and food systems). Evaluations of various LLMs across tasks reveal that LLMs allow to reduce the time required to generate experimental designs in sustainable protein formulation, outperforming expert human scientists across different metrics. However, they perform poorly in fine-grained tasks like menu design when simultaneously addressing climate impacts and human satisfaction. To overcome this limitation, the authors integrate LLMs with combinatorial optimization, achieving a substantial 79% emissions reduction in hypothetical restaurant scenarios without compromising customer satisfaction. The results underscore LLMs' strong potential, especially when complemented by optimization techniques, to accelerate sustainable food development and adoption.", "questions": "", "limitations": "", "rating": 2, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We greatly appreciate your feedback.\n# Baselines\nWe have added two baselines (expert chef, and the beef to chicken substitution you suggest), both of which we outperform, and three ablations (remove preferences component, remove diversity component, and remove both) for the menu design task. The chef was given the same set of instructions as the LLMs, but with more time: one hour. (Our o1-preview+IQP procedure takes ~3.5 minutes to run). We found that the chef did not always meet the ingredient availability constraint. Additionally, while the chef generated creative recipes, they lacked a strategy for reducing emissions, choosing to preserve several high-emissions dishes. \n\nBelow are our new results, compared with the original menu and our proposed method o1-preview+IQP. SD is in parentheses. \n\n|                                                           | Emissions     | Satisfaction | \n|-|-|-|\n| Original                                                  | 44.91 (38.83) | 8.40 (1.86)  | \n| o1-preview+IQP ($\\lambda=100$)                               | 8.70 (7.06)   | 8.44 (2.20)  | \n| o1-preview+IQP ($\\lambda=0$)                                 | 9.75 (7.50)   | 8.16 (2.25)  | \n| o1-preview+IQP ($\\lambda=100$, remove preferences) | 9.44 (7.26)   | 7.83 (2.56)  |\n| o1-preview+IQP ($\\lambda=0$, remove preferences)   | 8.54 (6.72)   | 7.12 (2.88)  | \n| Expert chef                              | 41.28 (40.38) | 8.20 (2.51)  | \n| Replace beef with chicken                                 | 17.57 (15.07) | 8.04 (2.22)  |\n# Novelty of empirical findings\nWe would like to clarify that we do have several novel findings, most notably that 1) LLMs can outperform expert food scientists in the generation of experimental designs for improving sustainable protein formulations and 2) our LLM+IQP algorithm outperforms an expert human chef and several baselines in the design of sustainable menus. Additionally, we characterize performance of LLMs on preference prediction tasks, finding that they can be useful for coarse-grained prediction but that further work is needed for fine-grained prediction. This is in addition to our novel task formulations. \n# Related work\nThank you so much for pointing us to the “predict-then-optimize” literature. We will cite it in the updated version. The ML contribution of our paper can be viewed as extending the predict-then-optimize framework by incorporating a component where the elements of the ground set (e.g. recipes) are generated, and applying LLMs to both the generation and prediction steps. Incorporating measures of LLM uncertainty and conducting additional theoretical analysis of this **generate**-predict-then-optimize framework is an ongoing area of work. \n\nHere we also address Vdt3’s references. Regarding [1] and [2] from their review, our framework differs in that the LLM is used to provide background knowledge on topics such as human preferences, to convert an imprecisely specified optimization problem (as is the case with many real-world planning, scheduling, decision making, etc. problems) to a precise optimization problem. Besides the sustainable menu design task we study, examples of applications include health coaching (“Generate a diet and exercise plan for achieving my goals while meeting constraints on ingredients and current injuries”), curriculum design (“Generate a curriculum that will maximize student engagement while meeting constraints on topics covered and class time”), and travel planning (“Generate a travel plan that meets my preferences and covers the following locations”). These applications cannot be readily addressed by the work in [1] and [2], which assume that the provided optimization problem is fully specified. Moreover, our work adds to the set of applications in this literature. Regarding Vdt3’s reference [3], we appreciate the pointer to this comprehensive survey. Our work differs from this literature due to our focus on leveraging background knowledge of LLMs to expand the class of reasoning problems that can easily be addressed with optimization tools. \n# Our contributions\nThe Application-Driven ML reviewer instructions state, “Originality need not mean wholly novel methods. It may mean a novel combination of existing methods to solve the task at hand, a novel dataset, or a new way of framing tasks or evaluating performance so as to match the needs of the user.” We have made contributions along all of these dimensions. We have combined LLMs and optimization to address the sustainable menu design task (outperforming an expert human chef and several other baselines), introduced two datasets not previously studied by the ML community, and introduced four novel tasks and associated evaluation metrics. \n\nWe agree that we do not have significant theoretical contributions. However, we do not believe that this is required for the Application-Driven ML track, which notes that the form of the contribution may differ from papers in the main track.\n\nThanks again!", "claims_and_evidence": "The paper provides substantial empirical evidence that LLMs can effectively help reduce the time required by food scientists in sustainable protein experimental design tasks, while improving specificity scores. In addition, combining LLM predictions with combinatorial optimization can successfully reduce emissions (79%) with respect to the baseline while maintaining consumer satisfaction, as shown in their human subject experiments. These findings are supported by concrete experimental results involving expert evaluations and online surveys.", "methods_and_evaluation_criteria": "The proposed methods and evaluation criteria make sense for the application. The integration of LLM predictions with combinatorial optimization addresses realistic trade-offs encountered in sustainable food design. The chosen datasets (NECTAR sensory panel and Food.com recipes) are appropriate. However, stronger baselines for menu design could further improve the evaluation, such as menus using lower-emission equivalent of the original menu (e.g., substituting beef with chicken) rather than only considering vegetarian or beef-free alternatives.", "theoretical_claims": "The proof of Proposition 6.1 is correct. This proposition is the only theoretical claim.", "experimental_designs_or_analyses": "n/a", "relation_to_broader_scientific_literature": "The paper's key contributions are (1) the application of LLMs for scientific discovery and human preferences modeling to the sustainable food domain, and (2) combining LLMs with optimization methods.\nFor (1), the paper extends prior work on LLM-based modeling of human behavior to the sustainable food domain, not currently explored in the literature. However, as discussed in the results sections, the observations align with the existing food science literature, resulting in few novel findings.\nRegarding (2), the paper cites [Yang et al., 2024] and [AhmadiTeshnizi et al., 2024] using LLMs for mathematical optimization problems. However, the coupling of LLM and integer quadratic programming is more closely related to the stream of data-driven optimization, in which the framework introduced in this paper has already been thoroughly studied.", "essential_references_not_discussed": "The paper's contributions align with the broader scientific literature on the \"predict-then-optimize\" method. However, the paper does not explicitly cite or discuss critical works related to this steam of literature in Operations Research and related journals, such as [Smart “Predict, then Optimize”, Elmachtoub and Grigas, 2021] and [From Predictive to Prescriptive Analytics, Bertsimas and Kallus, 2020].", "other_strengths_and_weaknesses": "The paper is well-written and reads well. The experiments involving human feedback (particularly the evaluation with 20 expert food scientists and the online surveys with a total of 552 participants) represent significant empirical contributions. However, the optimization approach introduced in Section 6 (combining LLM-based predictions with combinatorial optimization) is not particularly innovative, as it essentially adopts the well-known predict-then-optimize methodology where the prediction model is an LLM. Furthermore, given that the quantities to be predicted are highly uncertain quantities, such as human preferences and ratings, the predict-then-optimize framework is known to perform poorly with point prediction compared to other more robust approaches accounting for prediction uncertainty (see [From Predictive to Prescriptive Analytics, Bertsimas and Kallus, 2020]). Additionally, the paper does not include significant theoretical contributions.", "other_comments_or_suggestions": "n/a", "questions_for_authors": "n/a", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {"ethical_review_concerns": "n/a"}}, {"review_id": "s118cmiuEF", "reviewer": "Reviewer_YVD5", "summary": "This paper explores the capabilities of Large Language Models (LLMs) in a set design of prediction tasks associated with sustainable diets (mainly plant-based) that were based on sustainable food literature and collaboration with domain experts. The overall objective of the tasks consists of generating low-emission menu designs (based on the associated emissions induced by the ingredients used in the recipes) that preserve human satisfaction. The authors' main contribution is a framework that evaluates how good LLMs are in a zero-shot setup and a novel approach that involves defining the problem as an LLM-guided combinatorial optimization for the task of menu design. They tested the framework with six different state-of-the-art LLMs. Their framework uses two food-related datasets: NECTAR (about the sensory evaluation of food products) and Food.com (about recipes); they also include a list of recipes from delivery applications. They included twenty plant-based food scientists in evaluating their method based on four metrics (accuracy, specificity, complementarity, and time saved) in a blinded randomized test in which the scientist collaborated with an anonymous scientist (the LLM or a peer). The answers were homogenized in style to avoid detecting the use of a model.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We greatly appreciate your feedback.\n# Statistical analysis\nThank you for raising this point. We note that we could have analyzed this data in alternative ways, given both the small sample size ($n=60$ ratings across 30 products) and clustering in the data (namely, pairs of products that were evaluated by the same food scientist, or where their associated experimental design was generated by the same food scientist). We ran additional statistical tests and found that the statistical significance of our findings are robust to all analysis variations we tested, which includes methods that do not rely on the central limit theorem.  We will include this in the revised paper. The table of $p$-values across dimensions and methods is below.  For all dimensions the mean value was higher (better) for o1-preview than the human food scientists. \n\n| Test | Accuracy | Complementarity | Specificity | Percent Time Saved |\n|-|-|-|-|-|\n| $t$-test (as in submitted paper) | 0.121 | 0.350 | **0.003** | **0.0002** |\n| Paired $t$-test (taking into account the pairing across products) | 0.125 | 0.368 | **0.003** | **0.0003** |\n| Wilcoxon signed-rank test (exact, for paired samples, nonparametric, does not rely on normality assumptions) | 0.078 | 0.284 | **0.005** | **0.0007** |\n| Linear mixed model with random effect for evaluator ID  | 0.075 | 0.993 | **0.001** | **0.0002** |\n| Permutation test (nonparametric, exact, does not rely on normality assumptions) | 0.140 | 0.392 | **0.003** | **0.0003** |\n| OLS where we control for product ID, evaluator ID, generator ID as fixed effects | 0.1812 | 0.416 | **0.001** | **0.0006** |\n\nAdditionally, we would like to clarify our sample size for this task. Each data point was a rating of a human or LLM experimental design to improve a product. 30 products total were evaluated, for each of the two groups (human and LLM), yielding **$n=60$** ratings total. The total number of human evaluators was 20. As stated in Appendix D.1, in Phase 1 (generation phase), 15 food scientists were recruited to generate experimental designs for 2 products each, yielding 30 products with associated experimental designs. o1-preview was also prompted to generate experimental designs for each of the 30 products. Then, in Phase 2 (evaluation phase), another 15 food scientists (with overlap from Phase 1, but ensuring that no one evaluated their own designs) evaluated both human and LLM designs for 2 products each, on the four dimensions of accuracy, complementarity, specificity, and percent time saved. Then, for each of the four dimensions, a $t$-test was performed on the human vs. LLM scores, though as we show above the result is robust to alternative tests. We will make this more clear in the final version.\n# Evaluation criteria and satisfaction metric\nRegarding the comment “Something similar happens with the satisfaction metric, which relies on a proxy obtained by the LLMs, which the paper points out as biased towards omnivore options”, we clarify that for the menu design task, the satisfaction metric is based on responses of actual human participants, in response to the question, “How satisfied are you with your set of choices?” Please let us know if we misunderstood your comment; we will be happy to respond further. \n\nAcross tasks, we use a combination of automated metrics and those that rely on human judgment. In the preference prediction tasks, we use automated metrics (accuracy relative to ground truth sensory panel or recipe rating data). In the menu design task, the emissions computation is automated. \n\nWe agree that the human-reliant evaluation in the experimental design task could introduce bias, but we recruited 20 distinct expert human raters to minimize systematic bias.\n# Definitions of accuracy\nWe appreciate this point and will make the distinction clear in our revision.\n\n# Theoretical claim and typo\nYou are correct that the upper bound was overestimated, and should be $2Kϵ$ - thank you! We will fix this as well as the typo in the updated version. \n\n# Purpose of theoretical bound\nThe theoretical bound provides the insight that the maximum error of our approach can be bounded based on the number of items selected (rather than e.g. the size of the ground set) and the maximum item-level error of the LLM. We agree that future work could assess how downstream performance varies depending on the quality of the preference prediction method, and compare empirical results with the theoretical bound. \n\n# Supplementary material\nWe apologize for the insufficient documentation. Though we are not allowed to modify our submission at this stage, we commit to releasing a public GitHub repository that is well documented and provides full instructions for reproducing our results, other than information we cannot release due to IRB restrictions or the policy of the data provider (for the sensory panel data).\n\nThank you for your thoughtful review!", "claims_and_evidence": "Most of the claims in the paper are relatively well supported; they remain conservative, but that's natural given the nature of the setup: LLMs have biases towards omnivore diets, and satisfaction score depends on a proxy evaluation through the LLMs.\n\nThe results are analyzed statistically using a confidence interval of 95% confidence for different tests. However, those relying on human evaluators ($n = 20$) might hinder the validity of their statistical tests, considering the rule of thumb of having at least $n = 30$ for the central limit theorem to hold.", "methods_and_evaluation_criteria": "The datasets used in the paper are relevant to the proposed tasks.\n\nThe paper introduces a novel evaluation criteria that could have some caveats. It considers four metrics (accuracy, specificity, complementarity, and time saved) that rely on the open questions to a group of food scientists with moderate experience in plant-based food science and meat. This evaluation method seems reasonable given the nature of the tasks, but it could introduce biases given that it is entirely human-reliant.\n\nSomething similar happens with the satisfaction metric, which relies on a proxy obtained by the LLMs, which the paper points out as biased towards omnivore options.\n\nThere is confusion when the paper talks about accuracy; as for the task *Sensory Profile Prediction*, the definition corresponds to the ability of the LLM to describe the same sensory profile defined in the NECTAR dataset. For *Recipe Preference Prediction*, the accuracy corresponds to correctly estimating the recipe preference.", "theoretical_claims": "There is just one theoretical claim related to the LLM-guided combinatorial optimization problem in section 6.1. with a proof in the Appendix A. I believe that the theoretical derivation has a problem, overestimating the bound of $|f(\\hat{x}^*) - f(x^*)|$, following their justification, the right bound should be: $|f(\\hat{x}^*) - f(x^*)| \\leq 2 K \\epsilon$ the diversity term cancels out.\n\nHowever, this claim does not seem particularly important in their methodology. The selection of K is based on matching the length of a menu from another work (line 365).\n\nThe explanation of equation 2 has a typo (Line 361) when referring to $C_i$ instead of $C_j$, which is important to highlight because index $i$ and $j$ are used to refer to selection and constraint, respectively. This confuses the understanding.", "experimental_designs_or_analyses": "Experiments and procedures are rigorous and follow a methodic evaluation process. Their experiments are Menu Design, Sensory Profile Prediction, and Recipe Preference Prediction. I don't identify any concern with the way they performed the experiments other than the confusion that could exist between metrics like accuracy (which has two definitions depending on the task).", "relation_to_broader_scientific_literature": "The topic of interest is addressed by prior work in the food science domain without a relationship to the domain of artificial intelligence. The paper proposes a novelty based on introducing LLMs in food science-related tasks of their design. The tasks were designed around the data availability, and datasets like NECTAR and Food.com played an important role. They also relied on prior work on how human preferences can be distilled from LLMs.", "essential_references_not_discussed": "Not to my understanding.", "other_strengths_and_weaknesses": "*Strengths*:\n\n- The paper is well-written and easy to follow\n- It is novel to consider LLMs in the field of sustainable food; it could enable the adoption of low-emission alternatives that preserve human satisfaction\n- The inclusion of domain experts gives solidity to the proposed framework and experiments\n\n*Weaknesses*:\n\n- The theoretical bound in section 6 is not used; it could have contributed to some analysis of the traceability of the LLM-assisted optimization problem\n- The results based on humans could be statically invalid given the number of participants they included\n- The supplementary material offers resources to corroborate their framework, but it's hard to follow and doesn't provide any instruction", "other_comments_or_suggestions": "No additional comments.", "questions_for_authors": "1. What is the primary purpose of the theoretical bound introduced in 6?\n2. Do you agree that the sample size of the expert-assisted metrics impacts the applicability of the traditional methods to create confidence intervals?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "bHZYFcEfAp", "reviewer": "Reviewer_VYkF", "summary": "This paper explores the potential of large language models for sustainable food science. Specifically, this paper evaluates LLMs on four tasks, including experimental design, menu design, sensory profile prediction and recipe preference prediction. Then, this paper equips LLMs with combinatorial optimization to overcome the challenge (i.e., fail to balance emission target and satisfaction) in menu design tasks. Experimental results show initial success of LLMs for sustainable food science.\n\n## update after rebuttal\nI keep my scores unchanged, but I belive this is a good starting point for LLM in sustainable food science.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We greatly appreciate your feedback.\n# Fit for ICML\nThank you for raising this point. We believe that this paper is a strong fit for the Application-Driven Machine Learning track of ICML, defined as papers that “introduce novel methods, datasets, tasks, and/or metrics according to the needs of a real-world use case”.  We have made contributions along all of these dimensions. We have combined LLMs and optimization to address the sustainable menu design task (outperforming an expert human chef and several other baselines), introduced two datasets not previously studied by the ML community, and introduced four novel tasks and associated evaluation metrics. \n\nWe believe that publication of our work in ICML would be mutually beneficial for the ML community, serving to raise awareness of this important testbed for ML algorithms, and the sustainable food community. Climate Change AI was founded in 2019 by AI researchers to encourage more research at the intersection of AI and sustainability, and has held 12 workshops at NeurIPS, ICML, and ICLR over the past 6 years, suggesting that the intersection of AI/ML and climate change mitigation is indeed of interest to the ML community. Additionally, in recent years, sustainability organizations such as the Good Food Institute, Food Systems Innovation, New Harvest, and the Bezos Earth Fund have called for research at the intersection of AI/ML and sustainable food, particularly novel sustainable protein sources. Food systems are responsible for one third of human-caused greenhouse-gas emissions, and thus represent an important part of AI/ML for climate change mitigation efforts. Despite this, no prior work on sustainable food, to our knowledge, has been published in an AI/ML conference. Our work is a step in this direction. We plan to publish followup work in domain journals to also ensure impact in the sustainable food community.\n\n# ML contribution\n\nWe would like to clarify our ML contribution. For the recipe preference prediction, sensory profile prediction, and experimental design tasks, yes, we apply LLMs and analyze their performance, without contributing new methods. However, for the menu design task, we find that LLMs on their own do not adequately balance multiple constraints, and present a framework for combining LLMs with optimization techniques, which we find reduces emissions by 79% while maintaining patron satisfaction. We also provide an initial theoretical analysis of our framework (Proposition 6.1), showing that the error of our approach can be bounded as a function of the number of items selected and the maximum item-level prediction error of the LLM. While we currently only apply this framework to sustainable menu design, we believe it can also inspire future work. Besides the sustainable menu design task we study, examples of applications we are motivated by include health coaching (“Generate a diet and exercise plan for achieving my goals while meeting constraints on ingredients, cost, preparation time, and current injuries”), curriculum design (“Generate a curriculum that will maximize student engagement while meeting constraints on topics covered and class time”), and travel planning (“Generate a travel plan that meets my preferences and covers the following locations”). In these applications, both background knowledge (of nutrition, fitness, education, travel, human preferences, etc.) and optimization (to meet constraints) are necessary, and we show how to combine the background knowledge of LLMs with optimization tools.\n\nOur framework contributes to two strands of prior work in the ML and optimization communities. The first is the literature on LLMs for optimization modeling [1,2,3], which focuses on reducing barriers to the use of specialized optimization software via allowing users to specify optimization problems in natural language. Our work builds upon this literature, which assumes that the optimization problems are precisely specified. In our framework, LLMs are used to provide background knowledge on topics such as human preferences, to convert an imprecisely specified optimization problem (as is the case with many real-world planning, scheduling, decision making, etc. problems, such as the three mentioned above) to a precise optimization problem.  The second is the predict-then-optimize framework [4,5], as pointed out by reviewer XTjM. Here, our contribution is to add a generation step, in which an LLM generates the elements of the ground set (e.g. recipes, exercises, travel destinations) from which a solution is produced, as well as to apply LLMs for the prediction step. \n\n[1] AhmadiTeshnizi, A., et al. “OptiMUS.” ICML 2024. \n\n[2] Jiang, C., et al. “LLMOPT.” ICLR 2025.\n\n[3] Huang, C., et al. \"ORLM.\" arXiv 2024.\n\n[4] Elmachtoub, A. et al. \"Smart `Predict, then Optimize.’” Management Science 68.1 (2022).\n\n[5] Bertsimas, D. et al. \"From Predictive to Prescriptive Analytics.\" Management Science 66.3 (2020).\n\nThank you for your review!", "claims_and_evidence": "Yes.", "methods_and_evaluation_criteria": "Yes.", "theoretical_claims": "N/A.", "experimental_designs_or_analyses": "Yes.", "relation_to_broader_scientific_literature": "This paper explore novel perspectives of LLM application, i.e., sustainable food science.", "essential_references_not_discussed": "None.", "other_strengths_and_weaknesses": "Overall, this is a solid paper with clear motivation and novel empirical findings. However, in my view, it may not be a strong fit for ICML. A substantial portion of the paper focuses on sustainable food science, which may not align closely with the core interests of the machine learning community. Additionally, the machine learning component primarily involves the application of large language models, serving more as a tool than contributing ML methodology. Given this, I believe the paper might be better suited for journals focused on food science, where the audience has deeper domain expertise, and where the work may have a greater impact.\n\nThat said, since ICML also encourages application-driven machine learning, I am inclined to recommend a borderline accept for this paper.", "other_comments_or_suggestions": "None.", "questions_for_authors": "None.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: This paper presents an application-driven study on the use of LLMs for sustainable food systems, introducing four tasks, real-world dataset, and a framework combining LLM outputs with combinatorial optimization. Authors demonstrate empirical benefits across multiple tasks, including outperforming expert chefs in a menu design setting. Reviewers generally appreciated the originality, domain relevance, and careful experimental design. Some concerns were raised about limited ML novelty and real-world testing, but most were resolved/mitigated by the rebuttal. The work fits well within the scope of the Application-Driven ML track and contributes a new benchmark domain of increasing societal relevance. AC recommends accepting the paper for the application track.
Decision: Accept (poster)