NutriBench: A Dataset for Evaluating Large Language Models in Nutrition Estimation from Meal Descriptions

## Abstract

Abstract Accurate nutrition estimation helps people make informed dietary choices and is essential in the prevention of serious health complications. We present NutriBench , the first publicly available natural language meal description nutrition benchmark. NutriBench consists of 11,857 meal descriptions generated from real-world global dietary intake data. The data is human-verified and annotated with macro-nutrient labels, including carbohydrates, proteins, fats, and calories. We conduct an extensive evaluation of NutriBench on the task of carbohydrate estimation, testing twelve leading Large Language Models (LLMs), including GPT-4o, Llama3.1, Qwen2, Gemma2, and OpenBioLLM models, using standard, Chain-of-Thought and Retrieval-Augmented Generation strategies. Additionally, we present a study involving professional nutritionists, finding that LLMs can provide more accurate and faster estimates. Finally, we perform a real-world risk assessment by simulating the effect of carbohydrate predictions on the blood glucose levels of individuals with diabetes. Our work highlights the opportunities and challenges of using LLMs for nutrition estimation, demonstrating their potential to aid professionals and laypersons and improve health outcomes.
Our benchmark is publicly available at: https://mehak126.github.io/nutribench.html

## 1 Introduction

Effective nutrition monitoring and dietary management are essential components of healthcare, closely linked to the prevention and control of chronic diseases, including obesity, heart disease, diabetes, and cancer [, , ]. For instance, individuals with diabetes must accurately estimate the carbohydrate content of meals to determine appropriate insulin doses [, ]. Inaccurate carbohydrate estimation can result in high blood sugar (hyperglycemia) or low blood sugar (hypoglycemia), both of which can lead to severe short- and long-term health complications [, ].

Despite technological advancements in dietary assessment methods, nutrition estimation from self-reported dietary intake has shown limited accuracy and high user burden
[, , ]. A major limitation is that most modern nutrition datasets typically include tabular data  [, , , , , ] or meal images paired with nutrition information [, , , , , , ] but often lack natural language descriptions, restricting their usage and flexibility. For example, tabular database searches generally require an exact match for successful retrieval and multiple searches for meals with multiple food items, making the process time-consuming and burdensome []. In addition, image processing-based nutrition estimation systems are restricted to real-time predictions, pose privacy concerns [] and may encounter issues with food components being obscured in the image.

In contrast, using natural everyday language to describe meals provides greater flexibility, enabling users to detail various meal components and serving sizes without the constraints of real-time input. This also allows users to record meals in advance for planning or retrospectively for tracking.
We propose that Large Language Models (LLMs) are valuable tools for nutrition estimation from natural language meal descriptions due to their advanced language understanding and reasoning capabilities, vast internal knowledge and ability to refer to external sources to provide precise nutrition estimates. However, there are currently no existing datasets available to evaluate LLMs on this task.

To bridge this gap, we present NutriBench, a dataset comprising 11,857 natural language meal descriptions generated by GPT-4o-mini, derived from real-world global dietary intake data. NutriBench is human-verified and annotated with macronutrient labels, including carbohydrates, proteins, fats, and calories. To our knowledge, NutriBench is the first publicly available benchmark for evaluating the performance of LLMs on nutrition estimation from meal descriptions. NutriBench is constructed from dietary intake data from 11 countries, sourced from the What We Eat in America [] and the FAO/WHO GIFT [] datasets. We generate meal descriptions that include single and multiple food items, specifying serving quantities both in precise metric measurements (such as grams) and in natural language terms (such as ‘a tablespoon’ or ‘half a cup’) to capture the variety of meal description styles used in the real world.

We evaluate 12 leading LLMs, including open-source models (Llama 3.1 [], Llama 3 [], Gemma 2 [], and the Qwen 2 [] family), closed-source models (GPT-4o and GPT-4o mini []), and a medical domain-specific model (OpenBioLLM-70B []), on the task of carbohydrate estimation from natural language meal descriptions. The evaluation spans four prompting paradigms, including Chain-of-Thought (CoT) [] and Retrieval-Augmented Generation (RAG) []. Figure 1 shows an example of GPT-4o-mini’s output across different prompting strategies on a meal description from NutriBench.

Figure: Figure 1: GPT-4o-mini answers a query from NutriBench using different prompting strategies.
Refer to caption: x1.png

Additionally, we conduct a study with three professional nutritionists to obtain carbohydrate estimates on a sample of meal descriptions from NutriBench. Figure 2 summarizes the performance of the evaluated LLMs and the nutritionists. GPT-4o with CoT prompting achieves the highest accuracy of 66.82% with an answer rate of 99.16%. Most LLMs provide more accurate and faster estimates than the nutritionists, highlighting their potential as valuable tools for obtaining precise and accessible nutrition estimates from natural language meal descriptions.

Figure: Figure 2: Accuracy and Answer Rate for the evaluated LLMs across different prompting strategies on NutriBench.
Refer to caption: x2.png

Finally, we performed a real-world risk assessment by simulating the effect of carbohydrate estimates on the blood glucose levels of Type 1 diabetes patients.
An evaluation of blood glucose risk metrics across 44,800 simulations showed that nutrition estimates provided by LLMs can help Type 1 diabetes patients maintain their blood glucose within safe limits. Our experiments demonstrate the potential of LLMs to offer accurate dietary guidance, benefiting both professionals and laypersons and ultimately improving health outcomes.

Our contributions can be summarized as follows:

- •
NutriBench: We present NutriBench - the first publicly available natural language meal description dataset labeled with macronutrient and calorie estimates. NutriBench consists of 11,857 human-verified meal descriptions derived from real-world dietary intake data from *eleven* countries spanning across the world.
- •
Benchmarking LLMs: We evaluate *twelve* LLMs varying in size and expertise covering both open and closed source models with different prompting strategies including CoT and RAG on the task of carbohydrate estimation on NutriBench. This provides a comprehensive insight into the current capabilities of LLMs in nutrition estimation.
- •
Expert Nutritionist Evaluation: We conducted a study with *three* professional nutritionists who provided carbohydrate estimates for a subset of meal descriptions from NutriBench and measured the accuracy and speed of their predictions against those of the LLMs.
- •
Real-World Risk Assessment: We conducted a study simulating the effect of meal carbohydrate estimation on blood glucose levels of individuals with type 1 diabetes, demonstrating the real-world impact and potential health benefits of LLM-driven nutrition guidance.

## 2 Related Work

Nutrition Estimation. Most mainstream nutrition datasets such as FoodData Central (FDC) [], MenuStat [], FoodCom [], and Nutritionix [] feature tabular food nutrition information curated from diverse sources. However, tabular datasets based retrieval methods require users to use precise terminology to ensure successful and relevant retrieval. In addition, these methods do not support retrieving multiple items that may be present in a meal in a single search, making the process time-consuming and burdensome [].

Another popular method for nutrition estimation involves predicting nutritional values from food images. Such approaches may involve identifying food items followed by retrieval from a tabular nutrition database [] or directly estimating the meal’s nutritional breakdown from the image []. Many existing datasets contain images paired with nutrition information to facilitate research in this direction. These datasets may include images obtained from the web  [], real world, [, , ], or synthetically generated images  []. UMDFood90k [] provide a multimodel dataset with product images, text-based ingredient statements, and nutrient amounts.
However, image-based nutrition approaches are time-sensitive [], requiring users to capture specific pictures of their meals at the time of consumption and may encounter issues with food components being obscured in the image.

Enabling users to input meal descriptions in natural everyday language can help mitigate these issues. Initial exploration in this realm includes [, ], who use Convolutional Neural Networks to match food items from crowdsourced meal descriptions with an external tabular nutrition database. However, their data is not publicly released, preventing the evaluation of current state-of-the-art language processing approaches, such as LLMs, for this task.

Motivated by the lack of a standardized benchmark for nutrition estimation based on natural language meal descriptions, we introduce NutriBench, the first such publicly available benchmark, and evaluate state-of-the-art LLMs to gain insight into their current capabilities and limitations.

Large Language Models (LLMs). Large Language Models (LLMs) have made significant progress in recent years, with both closed-source models like GPT-4 [] and open-source models like Llama 3 and 3.1 [], Gemma 2 [], and OpenBioLLM[] enabling advancements in natural language processing as well as knowledge-intensive, reasoning, and cross-domain tasks including healthcare applications  [].
Despite their extensive internal knowledge, LLMs still suffer from issues such as hallucinations, incorrect or outdated information, and a lack of interpretability  [, , ].

Chain-of-Thought (CoT) [] prompting alleviates some of these issues by enabling models to reason about the answer step-by-step. Another promising solution is Retrieval-Augmented Generation (RAG) [], which provides the model with additional context relevant to the query by retrieving information from an external knowledge source. While previous works have identified shortcomings of both these approaches [, ], they have not been assessed on the task of nutrition estimation, which may uncover unique challenges.

Our work comprehensively evaluates 12 state-of-the-art LLMs with standard, CoT, RAG, and RAG combined with CoT prompting for carbohydrate estimation from natural language meal descriptions. We present a detailed analysis of how different prompting strategies affect LLMs’
performance in nutrition estimation in Section 5.

## 3 NutriBench Construction

NutriBench is derived from two source databases: What We Eat in America [] and FAO/WHO GIFT [], which collectively provide real-world dietary intake data from 11 countries, including Argentina, Bulgaria, Ethiopia, India, Italy, Mexico, Nigeria, Peru, Philippines, Sri Lanka, and the United States. After processing the meal data with nutrition labels, we instruct GPT-4o-mini to generate natural language meal descriptions to construct NutriBench. Examples of the meal descriptions can be found in Appendix A.

### 3.1 What We Eat in America

The What We Eat in America (WWEIA) survey is a national food survey jointly conducted by the U.S. Department of Health and Human Services (HHS) and the U.S. Department of Agriculture (USDA). Trained interviewers collect dietary intake data, including food names and portions through two 24-hour dietary recalls.
As of September 2024, WWEIA has completed 10 surveys from 2001 to 2020, compiling a total of 2,070,592 food items. To obtain nutrition labels, we utilize the Food and Nutrient Database for Dietary Studies []
, a comprehensive database that provides nutrient values for foods and beverages within the WWEIA data.

We cleaned the WWEIA data by removing entries with missing or invalid information (e.g., “Quantity not specified" portion sizes) and integrated data across the years into one comprehensive dataset. Since both the recorded food names and nutrition labels from the FNDDS change across the years, we use the 2017-2018 dataset as our reference point(^1^11The FNDDS 2017-2018 dataset is the most recent available data that aligns with the WWEIA survey prior to the pandemic.). Nutritional labels for each food item are calculated and normalized based on nutrition information derived from this reference data. Entries from the WWEIA that contain food names not found in the reference dataset have been excluded from our analysis.

### 3.2 FAO/WHO GIFT

The FAO/WHO GIFT [] is an open-access online platform by the Food and Agriculture Organization of the United Nations that provides individual food consumption data compiled from global national and sub-national food consumption surveys.

We compiled meals from 10 countries based on the availability of indicators that allowed us to separate lists of food items into distinct meals (such as eating timestamps and meal markers), nutritional data, and geographical diversity. The countries are displayed in Figure 3. For each meal, we obtained the food items, their quantities in grams, and the corresponding nutritional information. Since our goal is to generate natural language meal descriptions that include serving sizes expressed in everyday terms, we converted the food quantities from grams to common serving units. This was done by mapping the food items in the FAO/WHO GIFT dataset to The FoodData Central (FDC) [] dataset and applying a rule-based algorithm (Appendix F) to translate the quantities in grams into natural serving sizes. FDC is the food composition information center of the US Department of Agriculture (USDA) [] that provides detailed information on food items, including conversions from grams to natural servings.

#### Combining Food Items to Meals

Food items in WWEIA or FAO/WHO GIFT are recorded independently. Therefore, we merge food items into a single meal if they are consumed by the same person on the same day during the same eating occasion. For instance, a meal may include “a slice of buttered toast, a cup of black coffee, and half a grapefruit" consumed together. For each meal, we maintain either natural or metric serving sizes—ensuring there is no mix-up between the two. We compile 5,532 meal descriptions with natural servings, alongside the same 5,532 descriptions converted to metric servings from the WWEIA database. Additionally, Table 1 summarizes the number of meals and unique food items sampled from each country from the FAO/WHO GIFT.

Figure: Table 1: Unique meals and food items sampled from each country from FAO/WHO GIFT
Refer to caption: x3.png

### 3.3 Generating Natural Language Meal Descriptions

GPT-4o-mini Based Generation.
We instruct GPT-4o-mini to generate natural language meal descriptions from the collected meal data to create NutriBench . To encourage diversity, we prompt the LLM to produce five varied meal descriptions for each food item in a single generation, from which we randomly select one as the final query. Details of the prompts used are in Appendix B.1.

Human Verification. Human verification is conducted to ensure that the meal descriptions generated by the LLM contain no hallucinations or missing information. One of the authors acts as the verifier, confirming that the food names and serving sizes are accurately included in the final meal descriptions to match the nutrition labels. We identified two common mistakes made by GPT-4o-mini: missing food names and missing food servings. Additionally, we observed that GPT-4o-mini has a higher probability of omitting food names when a meal contains multiple food items and tends to make more mistakes generating descriptions with natural serving sizes compared to metric serving sizes.
For example, a meal description prior to human verification, “During lunch, I had a tasty medium crust pepperoni pizza and washed it down with a carton of 100% orange juice." was updated to, “During lunch, I had a piece of tasty medium crust pepperoni pizza and washed it down with a carton of 100% orange juice.”, after verification (note the change from a whole pizza to a single piece). More examples of this verification process are displayed in Appendix B.2.

## 4 Experiments

#### LLM Models

We conduct a comprehensive evaluation of *twelve* state-of-the-art large language models (LLMs) using NutriBench on the task of carbohydrate estimation from meal descriptions. The evaluation spans a range of open-source models, including Llama 3.1-8B, Llama 3.1-70B, Llama 3.1-405B-FP8, Llama 3-8B, Llama 3-70B [, ], Gemma 2-9B, Gemma 2-27B [], and Qwen 2-7B, Qwen 2-72B []. We also include closed-source models such as GPT-4o and GPT-4o mini [], as well as the medical domain-specific model OpenBioLLM-70B [].

#### Prompting Methods

We adapted *four* existing prompting methods with carefully designed prompts tailored for carbohydrate estimation.

- •
Base: The first baseline prompt involves instructing LLMs to estimate the carbohydrate content based on the meal description provided in the query with basic instructions.
- •
Chain-of-Thought (CoT) []: Due to the complexity of meal descriptions with multiple items in varying quantities, we hypothesize that the step-by-step reasoning induced by chain-of-thought prompting would reduce model errors by enabling the model to identify and reason about individual query components required to make the overall decision.
- •
Retrieval-Augmented Generation (RAG) []:
To further enhance the reliability of LLM predictions, we use RAG to ground their generations with nutrition knowledge retrieved from Retri-DB.
First, for a given meal query, we prompt the model to parse it into individual food components. Next, we retrieve nutrition information about each food item in the query through a nearest neighbor semantic similarity search and concatenate the results to form a comprehensive set of facts about the food components in the query. Finally, we provide this retrieved context along with the original prompts for LLMs.
- •
RAG+CoT: We combine the nutrition retrieval capability of RAG with step-by-step reasoning in CoT by concatenating the retrieved nutrition context with the CoT prompting for LLMs.

In all the cases above, we instruct the models to respond with ‘-1’ if they don’t know the answer to reduce the risk of potentially harmful predictions.

Figure 1 shows the predictions generated by GPT-4o-mini using the four prompting paradigms. We evaluate RAG and RAG+CoT with GPT-4o-mini, Llama3.1-70B, and Llama3.1-405B-FP8 only due to computation constraints. The prompts used for each method can be found in Appendix C.

#### Evaluation Metrics

We calculate the mean absolute error (MAE) to measure the deviation of the model responses from the true meal carbohydrates. In addition, we report accuracy (Acc@7.5) by considering the model output as ‘correct’ if the predicted value is within ±7.5g of the ground truth. This is based on the insulin-to-carbohydrate ratio, which indicates the grams of carbohydrates one unit of insulin can cover. While this ratio varies among individuals, it is generally considered 1:15 as a rule of thumb (^2^22https://www.tidepool.org/blog/optimizing-insulin-to-carb-ratios). Since we aim to improve insulin management, we maintain a conservative threshold of 7.5g on the absolute error to measure accuracy.

Finally, since we allow the models not to provide an estimate if uncertain, we also report the answer rate (AR), indicating the percentage of answered questions. Overall, the models should have a high answer rate and Acc@7.5, and a low MAE.

## 5 Results

Figure 2 presents the accuracy and answer rate of the 12 evaluated large language models across four prompting paradigms. Across all models and methods, GPT-4o with Chain-of-Thought (CoT) prompting achieves the highest accuracy of 66.82%, with an answer rate of 99.16%. Among open-source models, Llama 3.1-405B-FP8 with RAG+CoT prompting performs the best, with an accuracy of 59.89% and an answer rate of 96.05%.

### 5.1 Impact of serving descriptions, prompting strategies, and RAG

#### Natural Serving Descriptions are Preferred by Most Models.

We compare the performance of models on NutriBench with natural versus metric serving descriptions. Figure 5 shows the difference in MAE for each model between natural and metric serving descriptions.
We observe that most models exhibit higher MAE when using natural serving measures (e.g., “a cup of rice"), compared to metric measures (e.g., “80g of rice").
Notably, both GPT-4o and GPT-4o mini outperformed in the metric serving category. We hypothesize that this may be attributed to the inclusion of nutrition information associated with metric servings in the training data for the GPT family.

Figure: Figure 4: Natural servings are preferred by most models (orange), whereas the GPT family prefers metric servings (blue).
Refer to caption: x4.png

#### CoT Improves both Answer Rate and Accuracy, Especially on Complex Meal Descriptions.

Table 2 presents the answer rate and accuracy across the twelve models on NutriBench using Base and CoT prompting. We observe that CoT prompting significantly improves performance across both metrics.
Furthermore, while larger models generally outperform their smaller counterparts, as shown in Figure 2, CoT prompting allows smaller models to exceed the performance of larger ones. For example, GPT-4o mini with CoT prompting achieves higher accuracy and answer rate than GPT-4o with Base prompting.
This indicates that the step-by-step reasoning facilitated by CoT prompting improves the accuracy, demonstrating that even smaller models can achieve competitive or superior performance when paired with effective prompting strategies.

**Table 2: Accuracy and answer rate averaged over 12 models for Base and CoT.**
|  | Base | CoT |
| --- | --- | --- |
| Acc @ 7.5 | 43.24% | 47.46% |
| Answer Rate | 95.59% | 97.14% |

Looking more closely, we discover that CoT prompting helps mitigate increasing errors in complex meal descriptions.
Figure 5 presents a histogram of the meal descriptions in NutriBench with increasing food items and the corresponding MAE for GPT-4o under Base prompting (red) and CoT prompting (green). There is a sharp increase in the MAE with an increasing number of food items in the meal for Base prompting, CoT prompting significantly mitigates the error. This analysis demonstrates that enabling the model to parse meal descriptions into individual food items and perform explicit calculations through step-by-step reasoning improves the accuracy of LLMs, particularly for more complex queries.

#### RAG Does Not Always Improve Performance.

In our experiments with Retrieval-Augmented Generation (RAG), we built a retrieval database, Retri-DB, using data from FoodData Central (FDC) [], a food composition repository managed by the U.S. Department of Agriculture (USDA) []. First, we parsed meal descriptions into individual food items and retrieved relevant nutritional information from Retri-DB. This data was then used as context to prompt the LLMs for carbohydrate estimation. Figure 1 shows an example of nutritional information retrieved for two food items in the input query. Further details on Retri-DB construction are in Appendix  D.

Figure: Figure 6: Comparison of model performance with RAG on natural and metric serving queries
Refer to caption: x6.png

We evaluate the impact of RAG on GPT-4o-mini, Llama3.1-405B-FP8, and Llama3.1-70B. Figure 6 shows the accuracy and answer rates across four prompting methods for queries involving natural and metric serving descriptions. For metric-serving queries, RAG and RAG+CoT significantly outperform the Base and CoT for Llama3.1, indicating that the additional information retrieved for RAG provides valuable context that improves the accuracy of their predictions. However, for GPT-4o-mini, RAG shows only minor improvement over Base, while RAG+CoT performs worse than CoT. Similar to our hypothesis regarding the impact of natural versus metric serving sizes, we hypothesize that this may be due to metric serving nutrition information being included in the training of GPT-4o models. Therefore, RAG may not provide additional information to improve outputs but could misguide the original prediction by potentially noisy retrieval results.

On the other hand, for natural serving queries, while RAG shows some improvement over Base for the Llama 3.1 models, CoT-only always outperforms RAG+CoT. An inspection of Retri-DB reveals that for most food items, the nutritional data available in the retrieved context is for standardized metric 100g servings. We hypothesize that the LLMs are unable to successfully utilize this information when the food amounts in the query are described using natural terms. This suggests that alignment between the RAG database and the queries is crucial for improving results. A promising future direction is to augment Retri-DB with more natural servings for RAG to support knowledge-grounded predictions with LLMs.

Figure: Figure 7: Left: Food with higher carbohydrates tends to have larger MAE error. Right: Performance disparities across different countries in NutriBench.
Refer to caption: x7.png

### 5.2 Variation in LLM Performance Across Diets and Cultures

We analyze whether there are differences in the performance of LLMs based on dietary patterns and cultural contexts.

Correlation between meal carbohydrates and prediction error. Figure 7 (*Left*) examines the relationship between meal carbohydrate content and prediction error. We plot the frequency and MAE by GPT-4o with CoT prompting for varying carbohydrate content of single-item meals in NutriBench. We focus on single-food item meals for this analysis to avoid confounding the results with the effect of increasing food items in the meals. Our analysis reveals a positive correlation: the MAE increases as the carbohydrate content in the meal rises. This suggests that LLM predictions are likely to be more accurate for individuals with a generally low-carbohydrate diet compared to those following a high-carbohydrate diet.

Performance discrepancy across different countries.
We also measure the performance of LLMs on meal descriptions generated from various countries. Figure 7 (*Right*) displays the MAE for GPT-4o with CoT prompting across different countries in NutriBench. We observe significant disparities in the performance across these countries, with the lowest error of 2.20 on meals from Nigeria and the highest error of 15.12 on meals from Sri Lanka. Further analysis indicates a correlation between the average carbohydrate content per meal for these countries (blue bars in Figure 7 (*Right*)) and the MAE, with countries with a higher MAE generally tending to contain meals with higher carbohydrates. This pattern indicates that model performance may be influenced by the culinary variety and dietary habits of different cultures, such as the carbohydrate content in meals. Overall, our findings suggest that LLMs perform better for meals associated with certain cultural contexts and cuisines, highlighting the need for more equitable training practices that ensure diverse cultural and dietary representation.

### 5.3 Fine-tuning with FDC Data

To fine-tune our model, we use the FDC database to convert individual food items into natural language meal descriptions. This process yields a dataset of 600K meal descriptions (details provided in the Appendix). We then apply the Base prompting method to generate responses for all the meal descriptions, which serves as our training data. We select Gemma2-27B as the base model because it offers similar performance to Llama3.1-405B-FP8 while being significantly smaller in size, making it more suitable for fine-tuning. To fine-tune the model, we utilize qLoRA [] with a rank of 8, training for one epoch across 8 A100 GPUs.

**Table 3: Fine-tuning the LLM with nutrition data significantly improves MAE (lower is better), accuracy and answer rate (higher is better). Our fine-tuned model is denoted as Gemma2-27B-FT.**
| Model | GPT-4o | Gemma2-27B | Gemma2-27B | Gemma2-27B-FT |
| --- | --- | --- | --- | --- |
| Prompting Method | CoT | CoT | Base | Base |
| MAE | 8.61 | 13.74 | 13.33 | 9.57 |
| Acc @ 7.5 | 66.82% | 49.35% | 45.61% | 61.71% |
| Answer Rate | 99.16% | 96.79% | 88.65% | 100% |

As shown in Table 3, fine-tuning Gemma2-27B significantly improves performance over pretrained Gemma2-27B by reducing MAE, while also increasing both accuracy and answer rate. However, Gemma2-27B-FT still underperforms GPT-4o with CoT prompting, highlighting GPT-4o’s superior capability in carbohydrate estimation. This suggests that fine-tuning models from the GPT family could be a promising direction for future work to achieve an LLM-based nutritionist.

## 6 LLMs Outperform Nutritionists in Accuracy and Speed

We invited three professional nutritionists in a voluntary study to provide carbohydrate estimates for a subset of meal descriptions from NutriBench. We sampled 72 meal descriptions, including both natural and metric servings from the U.S. subset, since the nutritionists are based in the United States. For a fair comparison, we explicitly instructed the participants not to search nutrition apps or the web for carbohydrate estimates. We also provided them with three meal descriptions and their carbohydrates, identical to the few-shot examples given in the LLM prompts.

Notably, as shown in Figure 2, we find that several LLMs provide more accurate carbohydrate estimates than the nutritionists for the same 72 queries. Additionally, the nutritionists reported an average of 43 minutes spent estimating responses for all 72 queries. In contrast, LLMs can answer all 72 queries in just a few minutes, with GPT-4o-mini completing them in only 2 minutes.

These findings suggest that LLMs not only surpass nutritionists in the accuracy of carbohydrate estimates but also significantly reduce the time taken for processing queries. This highlights the significant potential of LLMs in helping to enhance the speed and accuracy of nutrition assessments, usable by both professionals and non-professionals.

## 7 Real-World Risk Assessment

We present a real-world risk assessment demonstrating the impact of nutrition estimation by simulating the effect of meal carbohydrate predictions on the blood glucose levels of individuals with Type 1 diabetes (T1D). To do this, we use the Tidepool Data Science Simulator (^3^33https://github.com/tidepool-org/data-science-simulator). The simulator includes a patient metabolism model that emulates how a virtual patient’s blood glucose will change over a period of time in response to external events (e.g.- carbohydrate or insulin intake). It also includes a pump simulator running the FDA-cleared Loop algorithm [] that delivers insulin to the virtual patient in response to the blood glucose levels and reported carbohydrate intake or additional insulin administration. In real-world scenarios, individuals with Type 1 diabetes either use such pumps or self-administer insulin to manage their blood glucose.

The patient metabolism model is based on three key parameters: the Insulin Sensitivity Factor (ISF), Carb Insulin Ratio (CIR), and the basal rate. We ran simulations for 20 virtual patients using CIR, ISF, and basal rates derived from real, anonymized data from Tidepool. Each simulation modeled a scenario where a virtual T1D patient consumed a meal at the start. The patient’s metabolic response was influenced by the actual carbohydrate content of the meal. We then simulated how the patient would manage glucose using estimated carbohydrates provided by nutritionists or LLMs to calculate insulin doses or input into their pump. Two scenarios were explored for each simulation: (1) the patient, not using a pump, calculates their insulin dose manually based on estimated carbs ‘x’ and their CIR (using the equation x/CIR), and (2) the patient, using a pump, enters estimated carbs into the pump, which adjusts the insulin dose automatically.

**Table 4: Simulation results: Carbohydrate estimates by GPT-4o with CoT prompting lead to the highest %TIR, lowest %TBR, and lowest BGRI**
| Carb Estimator | %TIR | %TBR | %TAR | BGRI |
| --- | --- | --- | --- | --- |
| Nutritionist 1 | 66.97 | 8.79 | 24.23 | 24.01 |
| Nutritionist 2 | 66.88 | 6.76 | 26.36 | 22.48 |
| Nutritionist 3 | 65.93 | 7.64 | 26.43 | 20.81 |
| GPT-4o (CoT) | 69.88 | 4.18 | 25.94 | 17.83 |

We simulated scenarios where patients obtained carbohydrate estimates for meals from nutritionists and GPT-4o. To capture a wide range of real-world conditions, we also varied the patient’s blood glucose at meal intake, setting it at 80, 120, 160, and 200 mg/dL. In total, across 20 virtual patients, 4 carbohydrate estimation methods, 4 starting glucose levels, 2 simulation setups, and 70 meals, we conducted 44,800 simulations. Each simulation ran for 6 hours, capturing the following blood glucose metrics:

- •
%Time in Range (%TIR): Percentage of time blood glucose remained within the safe range of 70-180 mg/dL.
- •
%Time Below Range (%TBR): Percentage of time below 70 mg/dL.
- •
%Time Above Range (%TAR): Percentage of time above 180 mg/dL.
- •
Blood Glucose Risk Index (BGRI): A combined measure of high and low blood glucose risk, calculated using the blood glucose risk curve defined by [].

Figure: Figure 8: Simulated glucose traces for a virtual patient using a pump with carbohydrate estimates from GPT-4o and nutritionists. The safe glucose range (70-180 mg/dl) is in green, low ($<$70 mg/dl) in red, and high ($>$180 mg/dl) in yellow.
Refer to caption: x8.png

Table 4 presents the metrics across the different simulation scenarios based on meal carbohydrate predictions from nutritionists and LLMs. We find that carbohydrate estimates made by GPT-4o with CoT prompting result in the highest TIR and the lowest TBR. Although Nutritionist 1’s estimates lead to the lowest TAR, low blood glucose poses a greater medical risk than higher levels []. Accordingly, the overall blood glucose risk is significantly lower with GPT-4o’s estimates compared to both nutritionists. Figure 8 shows simulated glucose traces for a pump-using patient consuming a meal at t = 0. Predictions by Nutritionists 1-3 and GPT-4o (with CoT prompting) are used in the four scenarios. We observe that while glucose remains within the safe range for simulations with GPT-4o and Nutritionist 3, those involving Nutritionists 1 and 2 drop below range, particularly for Nutritionist 1.

This case study indicates that LLMs can be valuable tools in real-life healthcare settings, helping to prevent risky outcomes and promote better health.

## 8 Conclusion

We present NutriBench, the first publicly available benchmark for evaluating the performance of LLMs in nutrition estimation from natural language meal descriptions. NutriBench comprises 11,857 human-verified meal descriptions, generated from real-world dietary intake data across 11 countries. We conducted extensive experiments to evaluate twelve state-of-the-art LLMs on carbohydrate estimation, with GPT-4o with CoT prompting achieving the highest accuracy, even surpassing professional nutritionists in both accuracy and speed.

#### Limitations.

Despite our efforts to include data from various regions, a limitation of NutriBench is that the dataset predominantly features meals from U.S. dietary intake. In future iterations, we plan to scale the global data, incorporating more countries and expanding meal coverage. We hope our work will encourage researchers to develop domain-specific LLMs for nutrition estimation, ultimately improving dietary choices and health outcomes.

## 9 Reproducibility Statement

We include NutriBench for the reviewers in the supplementary material uploaded with our submission. The dataset will be made publicly available for reproducibility. We describe our data creation process in Section 3, with the serving size conversion explained in Appendix F. The prompts used for meal generation are provided in Appendix B and for LLM evaluation are provided in Appendix C. The details of constructing the RAG database and fine-tuning data are provided in Appendix D and Appendix E, respectively.

## 10 Acknowledgements

We extend our sincere gratitude to Xuan Yang and Yifan Wei for the valuable initial discussions, data survey, and construction efforts for this project. We are also grateful to Xuezhi Wang for the insightful discussion and interpretation of our results. Finally, we thank Janice Macleod, Sandra Chmelnik, Susan R. McClendon, Jenifer Swartzentruber, Carla Cox, Meg Moreta, and Andrew Koutnik for providing perspectives and nutrition estimates for the human study as a professional nutritionist. Their collective contributions were essential to the success of our work.

## Appendix A More Examples of Generated Meal Descriptions

In this section, we give more examples of meal descriptions in NutriBench.

### A.1 Meal Description Generated from WWEIA

- 1.
For my snack, I poured myself a glass of refreshing white table wine.
- 2.
For dinner, I feasted on a McDonald’s Big Mac, enjoyed a large fast food order of French fries, and sipped on a large drink of caffeine-free fruit-flavored soft drink.
- 3.
For my evening meal, I savored a fresh regular bagel with a slice of American cheese melted on top.
- 4.
Tonight’s dinner consisted of a hearty 230g serving of macaroni noodles in a rich cheese sauce.
- 5.
During my snack time, I enjoyed 150g of ripe raw peach, complemented by 126g of banana and a crunchy 28g serving of baked potato chips.
- 6.
For a satisfying snack, I savored a 152g turnover that was filled with a scrumptious combination of meat and cheese and topped with a tomato-based sauce.

### A.2 Meal Description Generated from FAO/WHO GIFT

- 1.
For lunch today, I had a nutritious serving of 0.5 cup of raw lentils and 0.5 cup of crispy fried meat substitute cubes. (Argentina)
- 2.
This morning, I enjoyed a raw quince, slicing half of the fruit to savor its crisp texture and aromatic flavor. (Italy)
- 3.
For breakfast, I savored a mixture of half a cup of mung beans and a quarter cup of fresh coconut, providing a wholesome and energizing meal. (Sri Lanka)
- 4.
At dinner, I savored 100g of brown wheat bread complemented by 350g of rich quince compote. (Bulgaria)
- 5.
At lunchtime, I indulged in 466g of leavened bread, made with common millet and similar grains. (Ethiopia)
- 6.
For my snack, I had 110g of raw common guavas and 123.75g of fresh mango. (India)

## Appendix B Details on Meal Description Generation

In this section, we provide details about the generation of meal descriptions, including the prompts used for creating these descriptions and human verification examples.

### B.1 Prompts for Meal Description Generation

We show the prompt used for generating meal descriptions in Figure 9 and the prompt used for combining meal descriptions in fine-tuning section in Figure 10.

Figure: Figure 9: Prompts for meal description generation.
Refer to caption: x9.png

Figure: Figure 10: Prompts for meal description combination in fine-tuning.
Refer to caption: x10.png

### B.2 Human Verification

After generating five meal descriptions with GPT-4o-mini for each food item, we randomly choose one meal description as the final meal description. However, these descriptions still require refinement before practical application. Here are some examples of the raw generated descriptions and the descriptions after human verification:

Missing Food Names:
Before human verification: For a refreshing snack, I enjoyed 36.2g of strawberries alongside 47.8g of boiled eggs drizzled with 3.9g of vegetable oil. To complement the eggs, I added a touch of 10.1g of white sugar to sweeten my cup of whole cow milk. Additionally, I savored a tortilla, making for a delightful and satisfying snack experience.
After human verification: I enjoyed 47.8g boiled eggs, 3.9g vegetable, and a 69.2g tortilla with 0.2g salt and 10.1g sugar. In addition, I hvae 36.2g strawberries and 172.6g cow milk.

Missing Serving Size:
Before human verification:
I kicked off my day with a delightful breakfast of pancakes from frozen, complemented by a tablespoon of rich pancake syrup.
After human verification:
I kicked off my day with a delightful breakfast of one pouch of pancakes from frozen, complemented by a tablespoon of rich pancake syrup.
Before human verification:
I savored a chicken fillet biscuit from a fast food joint for breakfast, accompanied by a 20 fl oz bottle of cola.
After human verification:
I savored a chicken fillet biscuit sandwich from a fast food joint for breakfast, accompanied by a 20 fl oz bottle of cola.

Incorrect Serving Size:
Before human verification:
This morning, my breakfast consisted of a rich brewed coffee, enhanced with a flavored half and half cream, and I couldn’t resist having a whole banana on the side.
After human verification:
This morning, my breakfast consisted of a large brewed coffee, enhanced with an individual container of flavored half and half cream, and I couldn’t resist having a whole banana on the side.

## Appendix C Prompts for Carbohydrate Estimation

For Base and CoT methods, we query the LLM model with the meal description and some instructions as shown in Figure 11. For RAG, we will first parse the food description into food components. The parsing prompt is shown in Figure 12. Next, we will retrieve the nutrition information about each food item and finally provide LLMs with the retrieved context as well as the meal description. The RAG prompt for GPT-4o/GPT-4o-mini is shown in Figure 13. Prompts for other models, such as Llama and Gemma, follow their specific templates, though the content remains the same.

Figure: Figure 11: Prompts for carbohydrate estimation using GPT-4o-mini/GPT-4o without RAG.
Refer to caption: x11.png

Figure: Figure 12: Prompts for parsing meal description into food items using GPT-4o-mini/GPT-4o.
Refer to caption: x12.png

Figure: Figure 13: Prompts for carbohydrate estimation using GPT-4o-mini/GPT-4o with RAG.
Refer to caption: x13.png

## Appendix D Construction of RAG Database

We construct NutriBenchbased on The FoodData Central (FDC) [] dataset. We retained entries with complete macro-nutrient information, including carbohydrates, proteins, fats, and calories. After applying filtering and de-duplication, we finalized 450,182 food items with nutrition labels. Of these, 13,048 items have natural serving sizes. Since models process unstructured text information more efficiently than tabular data [], we use a rule-based transformation to convert the context to natural language. Specifically, for an entry containing a $serving\ amount$ and $nutrition\ amount$ for a particular $nutrient$, we convert it to the string "$serving\ amount$ has $nutrition\ amount$ $nutrient$".

## Appendix E Construction of Fine-tuning Data

We also leverage the FDC data to generate our fine-tuning dataset. We apply a rule-based transformation to convert each food item into a meal description similar to the process in building Retri-DB. In addition, we sample 98K food items, incorporating both natural and metric servings, and use GPT-4o-mini to generate meal descriptions for these items. To include multiple foods in descriptions, we randomly select 2 to 4 descriptions and use GPT-4o-mini to combine them into a single meal description. The prompt for combining meal descriptions is in Appendix B.1. As a result, we obtain a dataset of 158,246 GPT-generated meal descriptions and 450,182 rule-based meal descriptions.

## Appendix F Rule-Based Algorithm for Obtaining Natural Serving Units in the FAO/WHO GIFT Data

For converting the metric servings (grams) for food items in the FAO/WHO GIFT data to natural servings, we first mapped the food items in the FAO/WHO GIFT dataset to those in the FDC dataset by identifying the nearest neighbors in the OpenAI text-embedding-3-large embedding space and choosing a similarity score threshold of 0.5 to obtain valid mappings. A manual inspection of 100 sampled food items showed an accuracy of 85.5% with this threshold. For food items with valid FDC mappings, the serving amount in grams was converted to the nearest natural serving size. This was done by selecting the closest natural serving if it was within 10% of the actual gram amount. If no suitable match was found, the closest larger natural serving was chosen, along with a multiple to reflect the correct quantity. To avoid unrealistic servings (e.g., 10 tbsp of rice), if no multiple smaller than 3 was found, we selected the closest smaller serving and expressed it using fractions (e.g., ’quarter tablespoon’, ’half a cup’). For even smaller amounts, we applied volume conversions, such as 1/8 of a cup being equivalent to 2 tablespoons or 1/16 of a cup to 1 teaspoon.

## Peer Reviews

confidence: 4
contribution: 3
initial_rating: 5
presentation: 3
questions: - What measures are taken to verify the nutritional information accuracy of each meal description, especially considering human verification only involves a single author?
- Could you provide additional details on how you mapped food quantities to everyday serving sizes? For instance, how does the rule-based algorithm handle ambiguous measurements?
- How do you explain the higher error rates in carbohydrate estimation for high-carbohydrate foods? Does the model struggle with certain food types or serving sizes?
- What might be the underlying causes for cultural discrepancies in model performance, particularly for countries like Sri Lanka with higher MAE?
soundness: 3
strengths: - Nutribench is the first of its kind for the task of macronutrient estimation from the textual description of meals. 
- The paper benchmarks multiple LLMs and also explores/compares multiple prompting strategies. 
- The authors conduct an evaluation involving nutritionists as well. 
- The authors do a comprehensive analysis of performance across different dimensions.
summary: This paper presents NUTRIBENCH, a new benchmark dataset designed to evaluate LLMs on nutrition estimation from natural language meal descriptions. The dataset includes 11,857 meal descriptions annotated with macronutrient labels (carbohydrates, proteins, fats, and calories), generated from global dietary intake data. The authors assess twelve LLMs.
weaknesses: - The authors have relied on GPT4o to generate meal descriptions. There is a concern with this. Were all of the outputs by GPT4o verified by humans? 
- The fine-tuning experiment with Gemma2-27B shows improvements but does not address whether larger or different models would perform similarly with fine-tuning.
- The choice of specific prompting methods (e.g., why Chain-of-Thought outperforms other methods) should be discussed well.
- Although the paper mentions about the performance disparities across cultural diets, it does not explicitly address if these are due to model biases or data representation gaps or any unique characteristics of the specific foods.

---

confidence: 3
contribution: 3
initial_rating: 6
presentation: 3
questions: Questions:

(1) NutriBench consists of 11,858 meal descriptions. Was the human verification applied to all of them?

(2) It appears that human verification might induce subjective biases. Take the pizza case as an example, why should we update "a tasty medium crust pepperoni pizza" to "a piece of tasty medium crust pepperoni pizza"?

(3) Is the benchmark robust and reproducible? Even though the temperature can be set 0 to obtain more deterministic responses, it is uncertain whether the responses will be reproducible. It would be better to include additional experiments for this concern. 

(4) Does the prompt have to be strictly formulated to produce the results? Would paraphrasing the prompt significantly change the results?

(5) Would there be any biases when the data generator and carbohydrate estimator are identical? In this case, both are GPT-4o-mini.

(6) What is the x-axis in Figure 6?

(7) In the simulation experiment, it seems that the performance of different nutritionist varies significantly. Why is that?

(8) According to Figure 2, the human nutritionist performed worse than a lot of LLMs. When human made mistakes, what were those mistakes? Did LLMs perform better just because they held more knowledge? If each food is associated with a pre-defined value, why can't nutritionists looked those up if they were not sure of the knowledge?

(9) The authors discussed that previous benchmarks mainly focused on using images. I wonder whether it is feasible to caption the images from these datasets and expand the existing one. Additionally, why not caption the images instead of constructing a benchmark from the scratch?

Minor:

(1) It would be better to use verb tense consistently.
soundness: 3
strengths: (1) This study worked on an important issue which may influence diabetes management.

(2) Most of the writings are clear.

(3) In addition to a benchmark, this study also provided a preliminary experiments comparing LLMs with human experts. It also showed how it might be applied to real-world management via a case study.
summary: This study presented NutriBench, a natural language meal description dataset labeled with macronutrient and calorie estimates. It evaluated 12 LLMs covering both open and closed source models with different prompting strategies on carbohydrate estimation. It also conducted a study with 3 nutritionists to obtain carbohydrate estimates on a sample of meal descriptions. Through a real-world risk assessment by simulating the effect of carbohydrate estimates on the blood glucose levels of Type 1 diabetes patiens, it found that LLM can help Type 1 diabetes patients maintain their blood glucose within safe limits.
weaknesses: (1) The study might be much improved if it could provide more insights into the success and failure cases of the LLMs on the carbohydrate estimation task. When LLMs made wrong estimations, what were those mistakes? Was it because LLMs did not have the information, or it was more like a math operation issue. To me, it is unclear what capabilities of LLMs did this study intend to find out. If it is just for evaluating LLMs for carbohydrate estimation, the research contribution might be limited. It would be better if the authors could further explain how the findings of this study may inspire future works. Why is it different from other LLM benchmark papers? 

(2) The study should disclose hyperpoparameter settings in detail.

---

confidence: 3
contribution: 3
initial_rating: 6
presentation: 3
questions: More downstream tasks and comprehensive analyses on nutrition can be performed to generate more informative insights, compared to just providing carbohydrate predictions.
soundness: 3
strengths: 1. Data Collection: This study gathers 11,857 meal descriptions from various countries. This approach enhances text flexibility compared to traditional tabular data and addresses the challenges of acquiring image data.
2. Model Evaluation: The research evaluates the carbohydrate prediction capabilities of different large language models (LLMs). The results show that these models outperform nutritionists in both prediction speed and accuracy.
3. Social Influence: The paper conducts a real-world risk assessment by simulating how carbohydrate predictions affect blood glucose levels in individuals with diabetes. This demonstrates the potential positive impact of LLMs on public health.
summary: The paper introduces a benchmark called NutriBench, which includes 11,857 meal descriptions derived from real-world global dietary intake data. It employs Chain of Thought (CoT) and Retrieval-Augmented Generation (RAG) techniques to tackle the carbohydrate estimation task and evaluates twelve large language models (LLMs), such as GPT-4o and Llama 3.1. Ultimately, a real-world carbohydrate prediction task is conducted. The results demonstrate a significant improvement in both the accuracy and speed of carbohydrate estimation compared to traditional nutritionists.
weaknesses: 1. Limited downstream tasks: the paper only focuses on the carbohydrate prediction task. More downstream tasks can be performed, e.g., protein prediction, etc.
2. Over-reliance on description quality: the prediction performance generated by LLMs relies heavily on the quality (comprehensiveness, accuracy, etc) of description data.

---

confidence: 5
contribution: 3
initial_rating: 3
presentation: 3
questions: •	Note that Answer Rate and RETRI-DB are used before they are defined—consider moving up the definitions to the first place these terms are used.
•	Consider explaining why you used generative AI to write the natural language meal descriptions instead of humans.
•	Were all the foods eaten by people in 11 countries in the USDA’s Food Data Central? I’m surprised you had full coverage of globally eaten meals just from the US food database.
•	Please fix the caption for Figures 4 and 5.
•	In section 5.2, you comment that LLM predictions are more accurate for individuals on a low-carb diet. However, aren’t people on a high-carb diet more likely to get diabetes?
•	In section 5.3, why did you use the Base prompting method instead of CoT to generate responses when you said yourself that CoT is more accurate?
•	In real life, when someone has diabetes, how do they dose their insulin typically? I’m guessing they’re not calculating their carbs every time they eat.
•	In Appendix B.2, a couple of the human-verified responses actually seemed worse to me, with misspellings and unnecessary food words added (“sandwich” to “biscuit”).
•	Why is your FDC database only 450K foods? Doesn’t it contain over 1.8M foods?
soundness: 3
strengths: The greatest strength is the public release of the NutriBench dataset. Particularly impressive is that the dataset is based on real meals that people across 11 different countries eat. Another strength is the extensive experiments that were run, testing 12 LLMs, using three prompting strategies, comparing to nutritionists, and simulating the effect of carbohydrate predictions on blood glucose levels of individuals with diabetes. The takeaways for which LLMs perform best under which conditions (i.e., prompt, natural vs metric serving size, and diet/country) are important for researchers in this area. The real-world risk assessment with the Tidepool simulation of blood sugar levels is interesting, as are the LLM prompts in the appendix. Finally, this is an important problem with broad societal impacts—diet tracking is challenging, and finding ways to reduce user burden is critical for continued usage, which will help with obesity, diabetes, heart disease, and many other health conditions.
summary: This paper describes a new dataset, NutriBench, that contains over 10,000 synthetically generated natural language meal descriptions and corresponding macronutrients from matching foods in the Food Data Central database. They run experiments with 12 open and proprietary large language models, including varients of GPT, Qwen, and Llama on the task of carbohydrate prediction. They found that GPT-4o with Chain-of-Thought prompting resulted in the highest accuracy and response rate.
weaknesses: The biggest strength is the database, but the data that will be released and was included in the supplementary material contains only the natural language meal descriptions, no Food Data Central entities or their nutrition information. In addition, the only task was carbohydrate prediction, and the authors mention that the dataset contains only macronutrient information, but an LLM nutritionist should be able to predict protein and fat as well, and micronutrients as very important too. 11,857 meal descriptions is a small number, but is reasonable given they are human-verified. Another major weakness is the study in section 6 comparing LLMs to human nutritionists. It is understandable that human experts are slower, but instructing nutritionists not to search the web for carbohydrate estimates and then claiming that LLMs are more accurate seems quite problematic and results in a very misleading conclusion. Nutritionists do their job by using tools and resources. You can’t get an accurate measure of their ability to estimate carbohydrates if you take away the tools they use in the real world. The only actual takeaway here is that LLMs are faster, which is obvious. I love this work and really wanted to accept this paper, but can’t justify it due to these weaknesses.

Meta-review: This paper introduces NutriBench, a benchmark dataset with 11,857 natural language meal descriptions annotated with macronutrient data to evaluate LLMs for nutrition estimation. The authors conduct thorough experiments on 12 LLMs, explore prompting strategies, compare predictions with human nutritionists, and perform a real-world risk assessment for Type 1 diabetes management. Strengths include the release of the NutriBench dataset, which is a valuable contribution to nutrition research, the breadth of experiments, and the practical insights on model performance across diverse diets. The paper also addresses a critical problem with societal impact, such as improving diet tracking for health conditions like diabetes and obesity. While the study focuses only on carbohydrate prediction and restricts nutritionist tools in comparison, these limitations do not diminish its overall value. Some methodological concerns regarding comparisons to human nutritionists (e.g., restricting their use of external tools) should be clarified in future work. The NutriBench dataset and findings provide an important resource for future research and applications, justifying acceptance.

Decision: Accept (Poster)