DeLLMa: Decision Making Under Uncertainty with Large Language Models
Authors: Ollie Liu
Deqing Fu
Dani Yogatama
Willie Neiswanger
Venue: ICLR 2025 Spotlight (2025)
Keywords: large language models
decision theory
decision making under uncertainty
Primary area: other topics in machine learning (i.e., none of the above)
TL;DR: We introduce an inference-time reasoning procedure for reliable decision making under uncertainty with LLMs, drawing upon principles from classical decision theory.

Abstract: The potential of large language models (LLMs) as decision support tools is increasingly being explored in fields such as business, engineering, and medicine, which often face challenging tasks of *decision-making under uncertainty*. In this paper, we show that directly prompting LLMs on these types of decision-making problems can yield poor results, especially as the problem complexity increases. To aid in these tasks, we propose DeLLMa (Decision-making Large Language Model assistant), a framework designed to enhance decision-making accuracy in uncertain environments. DeLLMa involves a multi-step reasoning procedure that integrates recent best practices in scaling *inference-time reasoning*, drawing upon principles from decision theory and utility theory, to provide an accurate and human-auditable decision-making process. We validate our procedure on multiple realistic decision-making environments, demonstrating that DeLLMa can consistently enhance the decision-making performance of leading language models, and achieve up to a 40% increase in accuracy over competing methods. Additionally, we show how performance improves when scaling compute at test time, and carry out human evaluations to benchmark components of DeLLMa.

Published as a conference paper at ICLR 2025
DELLMA: DECISION MAKING UNDER UNCERTAINTY WITH LARGE LANGUAGE MODELS
Ollie Liu", Deqing Fu"; Dani Yogatama, Willie Neiswanger
Thomas Lord Department of Computer Science University of Southern Califomia me@ollieliu. com, (degingfu, yogatama, neiswang) @usc. edu
ABSTRACT
The potential of large language models (LLMs) as decision support tools is in- creasingly being explored in fields such business, engineering, and medicine. which often face challenging tasks of decision-making under uncertainty. In this paper, we show that directly prompting LLMs on these types of decision-making problems can yield poor results, especially as the problem complexity increases. To aid in these tasks, we propose DeLLMa (Decision-making Large Language Model assistant), framework designed to enhance decision-making accuracy in uncertain environments. DeLLMa involves a multi-step reasoning procedure that integrates recent best practices in scaling inference-time reasoning. drawing upon principles from decision theory and utility theory, to provide an accurate and human- -uudiabbee decision-make process. We validate our procedure multiple environments, demonstrating that DeLLMa can consis-
realistic decision-making tently enhance the decision-making performance of leading language models, and achieve up to a 40% increase in accuracy over competing methods. Additionally. we show how performance improves when scaling compute test time, and carry out human evaluations to benchmark components of DeLLMa.
INTRODUCTION
Large language models (LLMs) are rapidly gaining traction across many domains due to their po- tential for automating and enhancing broad spectrum of tasks (Bommasani et al., 2021; Bubeck et al.. 2023). One important potential use is in decision making under uncertainty, i.e., deciding which action to take given some set of possibilities, properly factoring in user goals and uncer- tainty about the world. The ability to make good decisions under uncertainty holds broad relevance across high-stakes tasks in fields such as business, marketing, medicine, aeronautics, and logistics (Kochenderfer, 2015; Peterson, 2017)- and its value is not limited to organization- but extends aiding individuals in making informed choices well. The ability of LLMMs analyze large quantities of data makes them potentially well-suited for sophisticated decision support tools, and ensuring that these models give accurate, context-aware recommendations could significantly augment human diciisionmakingg capabilities.
decisions
However, optimal decision making under uncertainty is often challenging. For humans, there exist frameworks from decision theory and utility theory (developed in fields such as economics, statis- ties, and philosophy) to provide a structured approach for better decision-making (Von Neumann & Morgenstern, 1944; Luce & Raiffa, 1989; Berger, 2013). Research has consistently demonstrated that without these frameworks, human decision-making can often be highly irrational, swayed by biases and incomplete information (Bazerman & Moore, 2012). Similarly, making decisions with LLMs faces its own set of challenges. Issues include the tendency to fixate on specific explanations or information without adequately balancing all evidence, and the inability to effectively handle uncertainty, manage biases, or align with a user's goals and utilities (Ferrara, 2023; Benary et al., 2023). Our paper presents experiments that exemplify these issues.
Furthermore, beyond merely making rational decisions, it is crucial to understand why an LLM made particular decision. This aids in building trust in the decision, assessing its quality, and improving any components that may lead to suboptimal outcomes. The ability to explain decisions and verify
"quull Contribution. Project website and code available https: /del mm..i thub. i0/-

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decision-making Secliitywhich we refer to human auditability essential for the practical application of LLMs to aid decision making in many real problems (Thirmaavukarauu et al., 2023).
In this paper, goal is to develop framework that enables LLMs to make better decisions under uncertainty. Our aim is not only to enhance cecision-makinng accuracy but also to allow human users to understand the rationale behind each decision. Drawing inspiration from prior work on multi- step reasoning like Chain-of- Chain-of-Thought (CoT) (Wei et al., 2022) and Tree-of-Thoughee al., 2023), which compute is scaled at inference time, we design procedure based on classical decision theory, originally designed for rational decision making under uncertainty by humans. Our approach involves three key steps: first, identify and forecast pertinent unknown variables given in- context information; second, elicit a utility function that aligns with the user's s goals; and finally, use this utility function to identify the decision that maximizes expected utility. We call our proposed framework Mel, short for Decision-making Large Language Model assistant.
(ToT) (Yao
We evaluate DeLLMa on real Deeision-making scenarios in agriculture and finance, and compare against existing strategies for LLM pecision-making including zero-shot (direct) prompting, self- consistency (Wang et al., 2022), and CoT approaches. We find that DeLLMa significantly enhances becision-making accuracy, with improvements of up to 40% increase in accuracy, particularly as the complexity and number of potential actions increases. Additionally, DeLLMa can consistently enhance performance across variety of leading language models, and its structure allows us to understand the rationale behind each decision. In full, our contributions are:
We introduce DeLLMa, a method for numan-auditable LLM decision making under uncertainty, employing a multi-step reasoning process at inference time based on classical decision theory. We evaluate components of DeLLMa. including the calibration of its state forecasting approach and a human agreement study assess its utility elicitation method.
On realistic decision- maaiing environments, we show that DeLLMa gives up to a 40% improve- ment in maniiiinnn-miinggggo accuracy over competing methods, and yields consistent improvements when deployed across multiple leading LLMs.
2 RELATED WORK
Exemplified by OpenAl al (OpenAI, 2024), a combination of recent advances in scaling inference- time reasoning- such as task decomposition and structured search- has brought forth superhuman performances on deterministic reasoning tasks (Hendrycks et al., 2021; Chen et al., 2021); but we nonetheless find that they are insufficient for decision-making under uncertainty. DeLLMa offers a specialized inference- time solution that scales favorbly with parallel sampling (Snell et al., 2024), a key ingredient for performant utility elicitation and decision optimization. Below, we summarize prior works pertaining to the DeLLMa framework.
LLMs for Decision Making. Prior works have leveraged LLMs for optimizing blackbox functions (Yang al., 2023; Nie et al., 2023; Shinn et al., 2024). These settings involve methods that make a substantial number of low-cost decisions (which do not incur a high price for suboptimality). Instead, we focus on single-step expensive decisions, particularly in the prescence of uncertainty, with a focus on the optimality of decisions. Additionally, a number of applied domains that involve decision making have started to explore LLM- baseed methods, such as in supply chain optimization (Li et al., 2023), medicine and health (Benary et al., 2023), and automated driving (Mao et al., 2023).
Uncertainty in LLMs. LLMs, without proper calibration, can be overly confident in their re- sponses (Si et al., 2022). Such pitfalls make them unlikely make reliable decisions under un- certainty. Prior work has aimed to solve this issue; one line of research involves asking LLMs for their own confidence, with or without additional finetuning (Kadavath et al., 2022; Lin et al., 2022; Mielke et al., 2022; Chen & Mueller, 2023; Tian et al., 2023; Xiong et al., 2023; Zeng et al., 2024). Referring to (Baan et al., 2023) for detailed survey, many recent advances (Lin et al., 2023; Feng et al., 2025; Falck et al.; Wong et al., 2023) adopt Bayesian inference framework to quantify and reason with uncertainties in LLMs. Other works have shown that tool usage (Ren et al., 2023), retrieval augmentation (Halawi et al., 2024), and model ensemble (Schoenegger et al., 2024) can improve calibration and forecasting capabilities of LLMs. Our framework can take advantage of these advances in LLM-based forecasting for improved decision making.
Inference-time Reasoning in LLMs. Many recent works have leveraged inference time compute to extend the computational power of language models (Merrill & Sabharwal, 2023; Sun et al.,
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Figure I: Given decision problem and contextual information prompt, DeLLMa (decision- making LLM assistant) maximizes an expected utility to select an available action. We illustrate the key steps of DeLLMa on prcision-making tasks in agriculture planning (top) and finance (bottom).
2024). These can be categorized into three main approaches: (1) instructing the model to generate intermediate reasoning traces (Wei et al., 2022; Yao et al., 2022; Zelikman et al., 2022; Zhuang et al., 2023: Ye et al., 2023; Shinn al., 2024; Cheng al., 2024), (2) decomposing complex reasoning problem into tangible components (Zhou et al., 2022: Radhakrishnan et al., 2023; Yao et al., 2023; OpenAI, 2024), and most recently (3) scaling the number of parallel samples to be postprocessed into a final solution (Wang et al., 2022; Snell et al., 2024; Brown et al., 2024; OpenAl, 2024).
METHODS
Preliminaries. Suppose that a decision maker needs to make a choice between a set of options to achieve some goal-i.e., has decision problem. We begin by formalizing such decision prob- lem, and afterwards describe how we approach decision making with LLMs. There are three main components to the decision problems that we will describe: actions, states, and utilities.
First, the actions are the possible options that a decision maker wishes to choose between. We use A to denote the space of actions, and AA for single action. Second, the of unknown states of nature are denoted O. In our formulation, we define a state 0 E θ to be any latent variable whose true value is unknown, yet affects outcomes relevant to the decision maker's 's goals. To perform optimal decision making, one must act while accounting for uncertainty over these unknown states.
The third component involves the decision maker's 's preferences for different possible outcomes. We formalize our framework for decision making under uncertainty using ufility theory, which can be viewed as "modeling the preferences of an agent as real-valued function over uncertain outcomes" (Kochenderfer et al., 2022; Schoemaker, 1982; Fishburn, 1968). A key element is the utility function (in some formulations, this is instead given terms of a loss function L). The utility function, denoted U : θ x A -> R, assigns scalar value to any state and action (8, a) θ x A. Intuitively, a higher utility means that the state-action pair yields a more- proee-refeeeble outcome for the user.
The goal of the decision maker will be to choose final decision a' A, which yields the highest possible utility, while accounting for uncertainty in the unknown states O.
Setup and Current Approaches. We first describe the setting in which we intend our framework to operate. Suppose human wishes to use this LLM assistant to help make a decision. They begin by describing a decision problem via a user prompt P. We formalize user prompt
Decision Making with LLMs:
triplet P AG,A, C), which includes: natural language description of the user's goal G, a list of n actions A = (aI,-.., an), and a passage of contextual information C, which might be, e.g., pages from report, or text-based representation of historical data.
Referring to the agriculture planning decision problem in Figure 1 as a running example, the goal g for farmer to maximize their revenue in the forthcoming year; the action set A lists the possible produce the farmer is considering planting (e.g., apples, avocados, pears); and the context C' consists of historical summaries of agricultural yields information about the climate around the farm.
is tempting to delegate such decision-making to LLMs with direct prompting. However, we observe that responses from conventional approaches, such as Self-Consistency and CoT. do not adequately balance available evidence, handle uncertain information, or align with user preferences; we show Sec, that these methods perform poorly, especially with increasing numbers of actions.
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DeLLMa: Decision-Making LLM Assistant. To help encourage improved decisions under un- certainty, we propose a framework that guides an LLM to follow the scaffolding of classical decision theory. By restricting LLMs to this scaffold we also explicitly see components of the decision- making process-e.st e. predictions of unknown states and utility function values-which provides human-auditability, allowing user to identify why given decision made by the model.
In our initial formalization of this framework, we restrict ourselves to a slightly curtailed class of problems, and thus make few simplifying assumptions. For example, we have assumed above that there are a discrete, enumerable set of n possible actions, i.e., A (a),..., an). We will also assume there is a discrete set of m possible states, Q (01..... B,), though m may be quite large.
Our framework will use an LLM to produce a belief distribution over the unknown states, given the input context C. We view this as a posterior belief distribution over the states, which we denote by (0 C). Implicitly, we are assuming that the LLM implies prior belief distribution (0),, given only the model weights or training data.
Our framework will also elicit a utility function, based in part on the description of the user's 's goals 9 E P. This utility function assigns a scalar value to any state-action pair (0, a). We denote this utility function as U00, a). Given these, the expected utility under our LLM of taking action a, given some additional context C, can be written
Uc(a) (3()) [U(0,a)] = Σ T(e ()).
AEE
Then, following the expected utility principle for rational decision making (Machina, 1987; Peterson, 2017), select the Bayes-optimal decision a", which maximizes the expected utility, written
a arg m Uc(a).
We call our framework DeLLMa, short for Decision-making Large Language Model assistant. MeI.MMa carries out this sequence of four steps- state enumeration, state forecasting, utility elici- tation, and expected utility maximization. A full description of DeL.MM is shown in the box below. In the following sections we give details on our specific implementation of each of these four steps.
DELLMA: AN ASSISTANT FOR LLM DECISION MAKING UNDER UNCERTAINTY
Input: Prompt = (9, A,C) consisting of user's goal G, actions A = (ai,..., an), and context C.
1. State Enumeration: Produce list of m states θ (0 (0....., ,,)). which are unknown quantities whose values are predicted to influence the user's goal G.
Section 3.1.
2. State Forecasting: For each state 0,, produce probabilitic forecast x (03) C). which describes the probability of different values of this state given context C.
Section 3.2.
3. Utility Function Elicitation: Produce a utility function U : (0), 0.) + RR which assigns a scalar Section 3.3.
value to each state-action pair (0z,a:), based on the user's goal G.
Expected Utility Maximization: For each action a,, compute the expected utility Uc(a.) = En(oje) (U(e, a.)), and return the decision a" = arg max;e (1....))) Uc(o.). b Section 3.4.
3.1 STATE ENUMERATION
As initial demonstration of DeLLMa, our goal is to develop a simple implementation that per- forms well empirically; however, each component in the framework can be extended or made more sophisticated. We first describe the strategy that adopt for enumerating space of relevant latent states θ (0,..... 0m). Given P as context, we prompt an LLM to identify latent factors that predicted to influence the user's goal G (see $C.2 for details on this prompt). Each latent factor is a string (a word or phrase), which can be viewed as describing a dimension of our state space e. We denote these & latent factors (f1, (fi..... fx).
For each latent factor, we prompt our LLM to generate plausible values of the latent factor (empir- ically find that it is sufficient set l be small, e.g., <5). For latent factor fj. we denote its
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plausible values as f..t where each plausible value is also string (a word or phrase). This process discretizes the state space, where each of the dimensions has & bins. We find that this strategy, while simple. yields empirically effective method for forecasting states (described in $3.2).
A single state 0 in this state space consists of one plausible value from each of the k latent factors, which we can denote by 8 = 0i E O. In total, this produces a discretized state space of size |0| = m ct. While this state space is too large to enumerate explicitly, we develop a procedure to forecast probabilities for these states in a scalable manner.
3.2 STATE FORECASTING
In the next step of De we form a probabilistic forecast of the unknown states, given informa- tion contained in the context C E: P. A number of recent works have shown that LLMs are capable of returning well-calibrated forecasts with respect to some provided information (Halawi et al., 2024; Schoenegger et al., 2024) (see $2). Here, we develop a relatively simple forecasting method that find works well empirically; though DeLLMa allows us to flexibly use other forecasting methods in the future (potentially leveraging search and retrieval of information). This step yields a distribution over the state space, which we can sample from to get a Monte Carlo estimate of the expected utility.
For each of the k: latent factors, and each of their & possible values f! we prompt our LLM to assign a verbalized probability score E { vver likely, likely, somewhat likely, somewhat unlikely, unlikely, very unlikely). ). In total, we must assign x scores. We provide all prompts for this probability score procedure in Figure 11. We then define a dictionary V that maps each verbalized probability score to a numerical value. Similar strategies converting from verbalized to numeric scores have been used with success in prior work (Xiong et al., 2023; Tian et al., 2023). After normalization, this yields a distribution over the state space O assuming independence between the latent factors, which we posit for computational simplicity. We sample states from this distribution by iterating through each of the latent factors, sampling according to its approximate marginal probability, and concatenating the samples. In Sec. 4.1 we directly evaluate this state forecasting procedure to show that it yields well-calibrated forecasts in real-data scenarios, and we conduct an ablation study in Sec. 4.3.
The full procedure is shown in Algorithm 1. Here, the Normalize function simply scales the weights instantiated in the marginal distribution to a well-defined probability mass function (PMF). We con- sider the sampled states be from an LLMM-defined proposal distribution ,LLM(P C), returned as output from Algorithm 1, which approximates the posterior belief distribution (( C).
Algorithm STATEFORECAST
Algorithm UTILITY
Input: LLM M, user prompt P (G, A),, plausibilty score mapping V. latent factors (fi,
Input: LLM M. user prompt (G.A,C) pro- posal distribution a (0 C). sample size s, mini- batch size b, and overlap proportion q.
ffk), and plausible values (fiie ... - Rt.).
# sample fixed states Va E A
for =1 do
SA + A x (A: 0: I < < |s/||]]>
Te(. T.(. C) -
SA Shuffle(SA)
Verbalized probability score
Q t 0
Pairwise comparisons
[u1....us] < M(PP.....
for = 1 to s with step 6b x (1 1 4). do
for = to
# Rank the minibatch
(f² |C) + Vlus]
R M (P, (B.,,,)) (8.45, a.o))
end for
Format into comparison f + QU FormatRank(R)
n((. C)- < Normalize (C)
end for
end for
return (I, fu () -I= ml C)
return U(,--) = Bradley Terry(())))))))))))))) e R
3.3 UTILITY FUNCTION ELICITATION
Next, we need a method to elicit (which to say: construct) a utility function U 1xxA= - R. which maps state-action pair to real value. An accurate utility function, which balances the preferences of human user with respect to the goal that they describe, is difficult to define directly in a general- purpose manner. There is long history of work on utility elicitation methods (Farquhar, 1984), which aim to construct a utility function from, e.g., pairwise preference data. Here, we combine these methods with large language models to try and automatically elicit utility function.
We conduct the following procedure. We first sample states from the forecast state distribution I LLLM (0 C), and from these form set of state-action pairs. We group these pairs into minibatches,
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and prompt LLM to rank the elements of each minibatch, given the user's goal 9 P. This LLM-based ranking of states-is procedure that can be broadly applied, and LLMs have history of being successfully used for similar comparisons (Lee et al., 2024; Qin et al., 2023). Based on these rankings, we are able to extract pairwise preferences, which we can in classic utility elicitation algorithms.
each item consists of an action and a particular instantation of
We show this procedure in Algorithm and discuss two implementations of the Format step: Rank2Pairs and Asllvs-All. Denoting (0, a) as the i-th preferred state-action pair of the mini- batch, Rank2Pairs converts a ranking of decreasing preference R. ((0, (().a)():""" ... (0)) a list of pairwise comparisons by adding (0, ()())) (0, ()))))) whenever < j. In contrast, One-vs-All assumes that the LLM is indifferent towards all but the top-ranked state-action pair, i.e,, {00 () > ()) 2 S I b}. This implementation may desirable when accurate com- parisons of certain suboptimal state-actions is challenging. We then make use of these preferences as training data for a Bradley-Terry model (Bradley & Terry, 1952) to elicit an approximate utility function U θ A + I with respect to the sampled state-action pairs. Finally, ditional ingredients are beneficial for scaling our inference-ting time reasoning, which help improve the accuracy and computational efficiency of utility elicitation: batching and variance reduction.
find two ad-
Batching, We implement a batched inference procedure that slices state-action samples SA = {(0, a)} into overlapping minibatches for ranking. We ensure that 4% of samples are shared be- tween two consecutive minibatches drawn from SA, where q is a hyperparameter that modulates a minibatch's 's degree of exposure to the preference the previous minibatch. Larger results in finer-grained preference at the cost of more queries. Effects of q are ablated in Figure 3.
Variance Reduction. Directly sampling |SAl state values from the proposal distribution xt I may lead to high-variance estimates of utilities. We instead sample |S // independent state values from x LLM create |A] duplicates, and pair them with each action (see Figure 5 in Appendix A).
In Section 4.3, we conduct ablation studies to validate our utility elicitation procedure, which shows high agreement rate between DeLLMa rankings and human preferences. We also study scaling laws of sample size and overlap percentage. We find that this type of specialized inference-time solution scales favorably against general-purpose systems such OpenAI ol (OpenAI, 2024).
3.4 EXPECTED UTILITY MAXIMIZATION
In the final step of DeLLMa, we compute the expected utility for each action, and then return the action that maximizes the expected utility. In particular, for each action, we compute Monte Carlo estimate of the expected utility using state-action samples (drawn from the state forecast distribution ..LM (Ø C)), as well as the elicited utility function. Note that these calculations are all performed analytically (not via LLM). We then approximate the expected utility Uc(a)
Uc(a) = (o(c) (U(0, a)) ~
Σ U(O,a), BES
(3)
ISI
given a set of state samples S C ④ drawn from our LLM-defined state forecast distribution, which an approximation of the LLM's posterior belief distribution about states given context C, i.e. s A (0 | C) R T(0 C). After computing the expected utility Uc(a) for each action,
DcLLMa returns the final decision: a" = arg max,eA Uc(a).
4 EXPERIMENTS
We evaluate the performance of DeLLMa on two decision making under uncertainty environments sourced from different domains: agricultural planning (Agriculture) and finance investing (Stocks). Both involve sizable degrees of uncertainty from diverse sources, and are representative of distinct data modalities (natural language and tabular) involved in decision making. We propose the follow- ing DeLLMa variants designed to assess algorithmic improvements, as outlined in Section 3:
DeL.LMaPPairs is the method using all techniques $3.3 and Rank2Pairs for utility elicitation.
DeLLMa-Topl is identical to DeLLMa-Paii but replaces Pank2Pairs with One-vs-All.
DeLLMa-Naive is a base version of DeLLMa-Paine where we sample multiple states per action and construct pairwise comparisons from single batch (iee, no batching variance reduction).

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Zcro-Shot
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Ma (Naive)
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Figure 2: Results on the Agriculture environment. Left: DeLLMa variants outperform baseline methods for varying numbers of actions. Right: We see that DcLLMa yields consistent improve- ment in decision-making accuracy across three families of leading LLMs.
For DeLLMM-Paars and Topl, we allocate per action sample size of 64 and a minibatch size of 32. We set the overlap proportion to 25% for the Agriculture dataset and 50% for the Stocks dataset due to budget constraints. For MeLiiMaN we fix total sample size of 50. As default LLM in experiments (except for comparisons across different LLMs), we use GPT-4 (Achiam et al., 2023)'.
We compare DeLLMa against three daselines-zero-shee self-consistency, and Chain-of-Thouge (where example prompts for these baselines are given in Figures 9, 10, 19, and 20):
Zero-Shot. Only the goal G, the action space A, and the context C is provided. We adopt a greedy decoding process by setting temperature = O.
ColiiConsistency (SC) (Wang et al., 2022). We use the same prompt in zero-shot, but with temperature = 0.5 to generate set of K responses. We take the majority vote of the K responses. Chain-of-Thoughh (CoT) (Wei et al., 2022). For decision-making tasks, there is no standard CoT pipeline. Inspired by workflows from decision theory, we create a prompting chain consisting of three steps: (I) ask for unknown factors that impact the decision; (2) given these, ask for their possibility of occurence; (3) then ask for final decision. Such a mechanism is similar to the DeI.L.a pipeline (see $3) but only consists of prompting.
Evaluation Metrics. For both datasets, our action spaces consist of set items, and we evaluate the performance of both DeLLMa and baseline methods by comparing the accuracy of their predic- tion from this set against the ground-truth optimal action (i.e., the action that maximizes ground- truth utility). We also report normalized utility-iee. the ground-truth utility of the action chosen by a given method, normalized by the optimal ground-truth utility-in Appendix B. We defer more involved decision- makiin under uncertainty problems, such as constructing weighted combination of actions (i.e., a portfolio), to future works.
4.1 AGRICULTURE
Data Acquisition. We collect bi-annual reports published by the United States Department of Agriculture (USDA) that provide analysis of supply-and-demand conditions in the U.S. fruit mar- kets'. To emulate real-life farming timelines, we use the report published in September 2021 as context for planning the forthcoming agricultural year. We additionally supplement these natural language contexts with USDA-issued price and yield statistics California..
We define the utility of planting fruit as its price x yield reported in the forthcoming year. We identify 7 fruits as our action set A A-apple, avocado, grape, grapefruit, lemon, peach, and pear-that are both studied in the September 2021 report, and endowed with these statistics in 2021 1 and 2022. We create decision making problems by enumerating all possible combinations of available fruits, related
resulting in 120 decision problem instances. For each decision-making instance, sections of the USDA report and current-year price and yield statistics as context. See Appendix C. and Figure 8 for additional details preprocessing, and Figure 13 for DeLLMa prompts.
We use GPT-4 checkpoint g-t4- -104-1106-preeiieee www. ers. usda. gtwlp i ons hetpsppuddeeeeeeeee 1 ?pubi 107539 nass. uada. gww st_tttt

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Figure 3: Left: Study on sample size and overlap percentage used by DeLLMa. Scaling the compute at test time produces better average accuracy. When ablating overlap percentage, we fix sample size at 16; when ablating sample size, we fix overlap percentage at 25% Right: Illustration of the DeI.LMa decision for the Agriculture dataset, showing of the actions, and two of the sampled states per action. Each weight w denotes the posterior probability T(((") |C).
From Figure 2, we observe that all DeLLMa strategies consistently outperform base- line comparison methods, especially for larger action sets. We plot decision accuracy here, but in- clude equivalent plots showing the utility of each strategy Appendix B. Among DeLLMa variants, the performance of Pairs and Top1 are consistent across the board; both are significantly better than Naive. This observation shows the benefits of the algorithms outlined in $3.3. We give more detailed analyses in the ablation study below. We also show comparison of DeLLMa deployed multiple leading LLMs (GPT-4 (Achiam et al., 2023), Claude-3 (Anthropic, 2024), Gemini 1.5 (Reid et al., 2024)), with consistent performance improvements across multiple model families. We refer readers to Appendix C and supplementary material for details on data, prompts, and responses.
Main Result.
How Good Are State Forecasts? We first evaluate the quality of forecast distributions. We manually annotate a set of ground truth values for states that the LLMs forecast within DeLLMa, and then compare this with the forecast state distribution. We compare the average calibration (ECE), and the negative log- likelihood (NLL), two popular metrics for assessing the qual- ity a probabilistic forecast. As shown in Table I, all mod- els achieve reasonable calibration performances, which correlate with the overall De prediction accuracy. We defer addi- tional comparisons Table 6, in which DeLLMa outperform randomized baselines. We conduct
Table I: An evaluation of state forecasting, showing ECE and NLL. Lower is better.
ECE +
NLL
GPT-4
0.062
1.11
Claude 3
0.142
1.20
Gemini 1.5
0.064
1.09
additional ablation studies to assess each DeLLMa module contributions in Section 4.3.
Failure Modes of Baseline Methods. One surprising observation is that, in the case of larger ac- tion sets, our baseline methods often underperform random guessing. A failure mode is that these methods elicit decisions that echo the sentiments presented in context, and they lack the ability to reason what-if scenarios that lead to utility changes. Our experiments that neither sampling reasoning paths, prompting the model to imagine the alternatives, nor aug- menting LLM with posterior belief substantially enhance the model's ability to reason with uncertainty. By conditioning on sampled states, Dei.LMa can avoid this pitfall while leveraging in-context learning to decide the preferred state-action pair. See Appendix C.4 and Figures 12, 17 and 18 for discussions on failure cases of baseline methods that are addressed by DeLLMa.
SC and CoT indicate
In addition performance improvements, our structured approach is endowed with human au- ditability. In Figure 3 (right), we show an abbreviated decision network, with actions, states, sam- pled latent factors, and derived utilities constructed from outputs DeLLMa agent. This modular approach to decision making can facilitate transparency and trust of LLMs in high-stake scenarios.
4.2 STOCKS
Making decisions involving financial investing requires handling variety of uncertainties; however, it is fundamentally different from the agriculture decision problems. Most evidently is the difference in input format-conte contexts for agriculture rely more textual information (summarizations from
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Zero Shot Ma (Naive)
Chain of Thought
Self Consistoncy DeL.MMa (Pairs)
De (Top
Usmoereir besht: dadl (APR grotth chrupt fir -R.
"ow SYDK
tenaeme beats imprrsnnont" grenss mygit
Accuracy
60
Preneen Iadiy
GnaiG good: avmust
Avszage
Number of Actions
Figure 4: Results on the Stocks environment. Left: We see that on average, DeLLMa-Topp outper- forms all baselines. Right: Illustration of the DeLLMa decision tree for the Stocks dataset, showing two of the actions and two of the sampled states per action.
USDA reports) whereas contexts for stocks involve tabular data (historical prices per share). Stocks decision problems are well suited to test L.LMs capability in reasoning on dynamic tabular data.
Data Acquisition. Similar to the setup in 1,.1, the action space A consists of individual stocks and we curate total of 120 decision problem instances of varying sizes. In our experiments, we choose popular stocks whose symbols are AMD, DIS, GME, GOOGL, META, NVDA and SPY. Unlike agriculture data where the context C are collected through USDA reports, we collect historical stock prices as the context for this problem. As illustrated in Figure I, each stock is presented with 24 monthly price in history. In preventing possible data leakage and promoting LLMs to their common-seen sense knowledge in making decisions, when using gpt 4-1106--11eeiiie checkpoint, historical price between December 2021 to November 2023 are provided as the context C. These historical monthly prices are collected via Yahoo Finance" manually by the authors.
as the LLM
The goal of the LLM agent is choose which stock invest December 1st, 2023, and sell on the last trading day of that month (December 29, 2023) so that the return is maximized. Detailed prompts are presented in Appendix D.I. We note that we only consider the simplistic setting- choosing one stock from a set of options
Main Results. Similarly, we compare three variants of DeLLMa with the three popular base- lines. Shown in Figure 4, most of the observations are consistent with those in the agriculture experiments-Dell..e -ea.LMMass variants outperform the baseline candidates. Unlike the agriculture set- ting. here DeLLMa-Naive only slightly improves over the baselines. We hypothesis this is due to the high volatility of stocks data, where inefficient sample size without variance reduction produces highly volatile predictors as well. This also validates the need for the design of the DeLLMa-Pail and DeLLMa- Top1 methods.
Additionally, DeLLMa-Tope Topl performs better than DeLLMa-Pairs in the stocks data. This difference may stem from the simplicity of utility elicitation from only the top action choice, which requires less data processing and may mitigate noise compared to enumerating all state-action pairs. We hypothesize that in high-volatility data like stocks, I.LMss may struggle with pairwise comparisons due to potential hallucination issues, particularly when the model attempts rank options without a clear ground truth. By focusing on the top choice, DeLLMa- Top1 could avoid accumulating noise from these internal rankings, achieving better performance.
4.3 ABLATION STUDIES
Scaling Laws for Inference-Time Time Compute. Referring back Figure 2, a potential explanation for the performance difference between DeL.Ma-Naive and Pairs/Topl is the discrepancy in sample size: for Naive, we allocate a fixed sample size (50) for all decision-making allocate a fixed per action sample size (64) for Pairs and Top1. We eliminate this confounder in our ablation study, by noting that with per action sample size 8 (middle subtigure in Figure 3), DcLLMa- Pairs/Topl achieve comparable performance to Naive, despite only receiving 16-48 samples in total for each problem instance.
instances, and we
fi nance. yahoo. com

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Table 2: Performance compar- ison across variations of our son against SotA inference-time reasoning models.
Table 3: Performance compari-
Table 4: Results on human evaluation of utility elicitation in DeLLMa. Details in $4.3.
state forecasting procedure.
GPT-
Clande
Gemini 1.5
Taok
DeLLMa
ol-preview
GPT4
Claude
Gemini 1.5
Unifeem
Underpeciiid
55.0%
58.3%
32.5%
34.2%
45.8%
22.5%
Agriculture
Stock
64.2%%
Agreement with Ulaman
68.4%
65.3%
65.7%
Ova-qaoilied
56.7%
35.8%
45.5%
DeLLMs
60.0%
4088%
47.5%
In Figure (left), we observe linear performance trends when scaling up overlap percentage and sample size. Intuitively, both higher overlap percentages (i.e., more exposure between minibatches) and larger sample size lead to construction of finer-grained pairwise comparisons and thus high quality approximate utilities. Furthermore, DeL.Ma-PPais consistently outperforms Top1, implying that a nontrivial portion of the pairwise comparisons are meaningful. However, we note that the number of required API queries scales linearly with both parameters, and users are advised to choose these parameters that balance performance and cost. We report statistics for API queries and prompt lengths for our methods in Table 5 of Appendix B.
State Forecasting Ablation. We now assess the quality of the state forecasting procedure by eval- uating DeLLMa in the presence of low-quality extraneous state information. On the agriculture dataset, we report in Table the ablation results from 3 variants for the propoal distribution nLLLM uniform, underspecified, and overspecified. We fix per action sample size of 16 and an overlap percentage of 25%, and refer readers to Appendix E for additional details.
We observe that the performance of the modified forecasting methods in GPT-4 and Gemini 1.5 are similar to full DeLLMa performance, and are better than our baseline methods. With these models, DeLLMa appears to be robust against misspecified, insufficiently representative, and extraneous forecast states. However, with Claude 3, ablation performance is more akin to zero-shot baselines. This discrepancy may be due to Claude's 's weaker performance in DeLLMa (c.f. Figure 2). Overall, our state forecasting procedure appears to be beneficial for DeLLMa performance.
Evaluation Against OpenAl o1. In Section 4, we have compared different prompting approaches while fixing the LLM backbone. We now evaluate OpenAl ol-a a class of more advanced models adept in inference-time time reasoning -gainst DeLLMa. Table 3 shows that best performances (attained with a per action sample size of 64 and overlap percenlage of 25%) can outperform ol with the zero-shol prompt (c.f. Figures 9 and 22) by wide margin, while attaining similar costs (DeLLMa, $0.09 to $0.37 vs. ol: $0.21 per instance)*, This study indicates the benefits of special- ized inference-time reasoning for decision making under uncertainty.
Human Evaluation for Preference Ranking. Lastly, we perform a study to evaluate the quality of utility elicitation in DeLLMa. We curate dataset of 412 pairwise state-action samples; human annotators (the paper authors and 5 external volunteers) are then shown pairs of these state-action samples and asked to annotate preference, based on the decision prompt P. These pairs are pre- sented in shuffled order to eliminate potential positional bias. We then compute the agreement rate between the pairwise annotations given by DeLLMa and those given by the annotators. Although this step is noisy, we see strong agreement between LLM and human annotations, as shown in Ta- ble 4, across multiple L.Mss. We also find that our inter-annotator agreement rate 67.0% (++6.3),, which is on par with the human-LLMM agreements. Additional details of our annotation procedure are reported in Appendix F.
CONCLUSION
We propose DeLLMa, a framework designed to harness LLMs for decision making under uncer- tainty in high-stakes settings. This is a structured approach in which we provide process for inference-time reasoning in LLMs that follows the principles of classical decision theory. We then develop a feasible implementation with LLMs. Through experiments systematic failure of popular prompting strategies when applied to this type of decision making task, and demonstrate the benefits of our approach to address these issues. The modularity of our frame- work avails many possibilities, most notably auditability and using LLMs for a broader spectrum of probablistic reasoning tasks.
real datasets, we highlight
'DeI. MM costs be further reduced by 50% with the OpenAl hatch API.
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REPRODUCIBILITY STATEMENT
To facilitate reproducibilityy have included the following artifacts within our paper and supple- mentary materials:
Data. All DeLLMa data, prompts, and LLM inference results for main experiments are given in Section 4. We also include all prompts in Appendices C.2 and D.1.
Code. Our implementations for the zero-shot, self consisteney, Chain-of-Thoughe and DeLLMa methods are included in the supplementary material. While these inference results are included, reproducing them require OpenAl, Anthropic, and Gemini API access. We provide evaluation script in results. sh for reproducing all numerical results.
ACKNOWLEDGEMENT
We thank Yu Feng, the USC NLP Group, and anonymous reviewers for valuable insights, discus- sions, and comments.
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APPENDIX
A UTILITY ELICITATION DETAILS
In Figure 5, we show an illustration of the overlapped batching and variance reduction strategies that DeLLMa uses in its utility elicitation procedure (described in detail in Section 3.3).
18 ..)
2
Pair
2 a
( a
( ..)
(0 (2)
(6 .
Shuffle
(g 3)
3) d
(0t) .
(0 a
.a))
(# a
(6 de)
1 ..a))
(0
1 a,)
(013 (
(6 .
(
(0 4
(0 .g)
(0 ..)
Figure Schematic diagram of overlapped batching and variance reduction.
B ADDITIONAL RESULTS
In Figures 6 and 7 we show the normalized utility (i.e,, ground-truth utility of the chosen action, normalized by the maximum ground-truth utility) of each method. These results can be contrasted against the accuracy of each method's prediction of the optimal action Figures 2 and 4.
Thought
Consistency
(Nuive)
DeLLMa (Top
(Puis)
100
Normalized Udbty
95
90
85
80
75
Avenge
Number of Actions
Figure Normalized utility for the Agriculture dataset. Across all action sizes, DeLLMa- Pairs/Topl outperforms other methods, including DeLLMa-Naive. Similar to accuracy, DeLLMa- Pairs slightly outperforms Topl, which is consistent with our hypothesis that the complete ranking generated from GPT-4 is meaningful.
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Zamo-Shet
Chrineef-Thoighe
Self-Comitteeey
DeLLMs (Naive)
DelLMa
DeLLMs (Prirs
Normalized Usilty
80
Averaee
Number of Actions
Figure 7: Normalized utility for the Stock dataset. DeL.La-Topp is competitive againt Pairs, sug- gesting that financial decision making may be challenging scenario for GPT-4 elicit preference unequivocally.
Aside from performance improvements, we also compare the cost measured in terms of prompt length and API calls in Table 5. For the Agriculture dataset, DeLLMa-Naive is comparable SC in terms of prompt length and significantly outperform the latter, while only requiring 20% of API calls. MaLLMa--Pair//ooe large sample sizes.
can push the performance further, but incurs higher cost, especially for
Table Number GPT-4 API calls and word counts per decision-maie makinn instance (with action set size 4 for the Agriculture dataset) across all methods discussed in Section 4.1. For DeLLMa-Naive we set the total sample size to 50. For DeLLMa-Pairs, we fix the overlap percentage to 25% and vary the action sample size from 16 to 64. D has the same statisties as DeLLMa-Pairs since they only differ in post processing.
Zero-Shot
CoT
SC
D-Naive
D-Pairs (16)
D-Pairs (64)
API Calls
1
3
3
10
Token Counts
495
1946
2477
2629
5181
20639
We provide additional results on forecasting performance measured by the expected calibration error (ECE) supplement Table by focusing on two of statistics in Table
ECE under the uniform distribution forecast (CCE-nnifomm).
ECE under the LLM forecast, but with random choice as the ground truth (ECE-Random), We report the standard deviation from 100 random trials.
To calculate the ECE, for fixed set of latent factors (e.g., fruit price change, climate conditions), we manually find and annotate ground truth values - using the USDA report and web search. We can then compute metrics such as the expected calibration error (ECF) for forecasts of these latent factors given by model. Together, these results indicate that our state forecasting algorithm attains much improved performance in comparison with these baselines.
Table Comparison of ECE across different proposal distributions against randomized baselines.
ECE
ECE-Random (+SD)
ECE-Uniform
DeLLMa (GPT-4)
0.062
0.135 = 0.092
0.333
DeL.MMa (Claude 3)
0.142
0.157 + 0.084
0.333 0.113 + 0.083
DeLLMa (Gemini 1.5)
0.064
0.333
C ADDITIONAL DETAILS FOR AGRICULTURE
C.I DATASET CURATION
To reduce context length, we first extract executive and per-fruit summaries of the report, reducing the context length from 8,721 words to around < 700 words. These summaries constructed
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per-model basis: each LLM (GPT4, Claude-3, and Gemini 1.5) is prompted to generate its summaries. We proof-read these summaries to ensure that they do not contain any factual errors. For each decision making instance, use the executive summary, the summaries relevant to the fruits in consideration, and their current-year price and yield statistics as context. We provide these summaries in supplementary material, and refer readers to the prompt for summarizing the report in Appendix C.2, and report concrete summaries and statistics in supplementary materials.
C.2 PROMPT USED BY AGRICULTURE
Summary Prompt In Figure 8 we present the prompt used for summarizing the context in our Agriculture experiments. This prompt shared across all models tested.
Zero-Shot Prompt In Figure we present an example prompt for zero-shot experiments consist- ing of (G, C, A), with GPT-4 summaries generated from Figure 8 as context. We fix the prompt format for all LLMs and select the corresponding summaries for each LLM.
Coli-Consi Prompt We adopt the same zero-shot prompt as the one shown in Figure 9, but take majority vote from K reasoning paths as the final prediction. Our preliminary study finds that LLMs tend to be very confident in their decisions, and even with much higher temperature than 0.5, often all K independent runs yield consistent decisions. We thus K 5 to balance cost and performance.
Chain-of-Thouur Prompt We design multi-prompt CoT procedure that closely emulates our DeLLMa agent, consisting of three steps: (1) ask for unknown factors that impact the decision; (2) given these, ask for their possibility of occurence; (3) then ask for final decision. This procedure differs from DeLLMa agents as it does not use an external program for utility elicitation and expected utility maximization, but delegates each step of the process to an LLM. Example prompt can be found Figure 10.
DeLLMa Prompt Similar the CoT Prompt, DeLLMa a multi-prompt procedure (1) ask for unknown factors that impact the decision; (2) given these, ask for their possibility of occurence (i.e. a belief distribution). Instead of directly deciding on an action, DeLLMa samples state-action pairs (via an external program) from this belief distribution, and leverage LLM to elicit utility A + R from pairwise comparisons. From here, we use another external program
function U: to search for action that maximizes the expected utility.
In Figures 8 and 11 we present the prompt for state enumeration and state forecasting (i.e. step (1) and (2)) for the Agriculture dataset. Our implementation combines these two modules, but in the future they should be separate as our framework continues to progress. For example, state enumeration may leverage retrieval-augmented generation Lewis et al. (2020) for generating high- quality unknown factors, and state forecasting may resort to tool usage to delibrate calibrated belief' distributions from quanitative analysis.
In Figure 13, we provide a DeLLMa prompt to generate ranked comparisons from sampled state- action pairs. These state action pairs are sampled from the belief distribution via an external pro- gram, written in Python. We provide all our implementations in the supplementary material.
C.3 AGRICULTURE DATASET RESPONSES
Summaries See Figure 14 for formatted GPT-4 summaries from the USDA agricultural report and statistics. Claude-3 and Gemini summarics follow the exact same format, but differ in LLM generated contents.
Zero-Shot Responses See Figures 12, 17 and 18 for sample GPT-4 responses zero-shot prompts.
Colf-Consisteccy Responses SC responses follow the exact same format as those Figures 12,
17 and 18.
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Chain-of-Thought Responses
See Figure 15 for curtailed version of GPT-4 CoT response.
DeLLMa Responses
See Figure 16 for curtailed version of GPT-4 DeLLMa response.
C.4
FAILURE CASES ON THE AGRICULTURE DATASET
In Section 4.1, postulate that failure mode of our baseline approaches is that they lack the ability to perform probablistic reasoning, but are rather following the sentiment presented in context. Here, qualitatively test this hypothesis, by showcasing prompts and responses from our Zero-Shot experiments. We present instances in Figures 12, 17 and 18 that are not able to make optimal prediction with baseline methods, but solved by DeLLMa agents.
Within each figure, we first present P, followed by the (incorrect) decision and explanation gen- erated from the Zero-Shot baseline, and then present the top-ranked state action pair generated by DeLLMa along with its explanation.
In the case of deciding between apple and grapefruit Figure 12, GPT-4 presumes that a high price for grapefruit is sustainable due to the presence of extreme weather conditions in the previous year. With COT, the model reasons with some counterfacual scenarios, such as more suitable weather for the forthcoming year, but not in systematic way. With DeLLMa, provide these list of counterfactual scenarios as context, and leverage the model's ability for situational thinking and ranking elicit an informative preference. We make similar observations for Figures 17 and 18.
D ADDITIONAL DETAILS FOR STOCKS
D.1 PROMPT USED BY STOCKS
Zero-Shot Prompt Similar the agriculture setup. we first present sample zero-shot prompt in Figure 19. The zero-shot prompt consists of three main parts (see Figure for visual illustrations): (1) an enumeration of action spaces (here AMD or GME), (2) provided context (here, historical stock prices between December 2021 to November 2023), and (3) the goal of the user (choose only one stock to maximize their profit via investing in stocks).
Self-Consise Coff-Consistecy Prompt
SC prompts are identical zero-shot prompts.
Chain-of- ofToughhh Prompt Similarly, we examplify a prompt for CoT. Notably, we break the chain into three parts in Figure 20: (1) Enumerating unknown factors, (2) Enumerating n nLLM(. C), and (3) making the final decision given the previous responses and context.
DeLLMa Prompt Finally, we showease DeLLMa ranking prompts where the LLM is asked to provide comprehensive rankings of given state-action pairs.
D.2 STOCK DATASET RESPONSES
Zero-Shot Responses
See Figure 22 for sample GPT-4 responses zero-shot prompts.
Self-Consistency Responses
SC responses follow the exact same format those in Figure 22.
Chain-of-Thought Responses
See Figure 23 for curtailed version of GPT-4 CoT response.
DeLLMa Responses
See Figure 24 for curtailed version of GPT-4 DeLLMa response.
E ADDITIONAL DETAILS FOR ABLATION STUDIES
we maintain the same set of states, but replace the forecast distribution LII with uniform distribution.
Uniform:
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Underspecified: reduce the diversity of our set of states by removing a latent factor fk+ chosen at random, and then perform state forecasting as usual.
Overspecified: in addition to the original set of latent factors, we add small set of additional factors, which are intentionally unrepresentative or redundant. This represents a case where the latent factors contain extraneous information, which may confuse the LLM backbones.
F DETAILS ON ANNOTATION PIPELINE
For each pair of state-action values, we ask human evaluators (paper authors and 5 external volun- teers) to annotate which state-action tuple is more preferred the other, and compare annota- tions against preferences elicited by LLMs. To reduce bias in this procedure, we present these pairs in shuffled orders, i.e. is always the annotators. For each pair, we ask the annotators to either label them as 1 (state-action tuple is preferred), 2 (state-actis tuple 2 is preferred), or 0 (uncertain). We then evaluate annotator LLM agreement after evaluation is concluded. In total, this procedure yields 412 annotations.
that (s, a) (s,a)p a)2 if {(s, a) (s, a)2) is presented to
From these annotation results, we measure the agreement between each pair of annotators, and compute inter-annotator agreement by micro-averaging all agreements. This procedure yields an average agreement of 67.0% (+6.3%), which on par with human-L.LM agreements.
G LICENSE FOR EXISTING ASSETS
Large Language Models DeLLMa is built on top of frontier class LLMs such GPT-4 (Achiam et al., 2023), Claude-3 (Anthropic, 2024), and Gemini 1.5 (Reid et al., 2024). We are not aware of their licenses, but ascertain that abide their respective terms of service (ToS)6 for our usage.
Agriculture Dataset the U.S. Department of Agriculture. These assets are freely accessible under the Freedom of Infor- mation Act. We ascertain that abide to the USDA ToS?.
The Agriculture dataset is curated from statistics and reports published by
Stock Dataset The Stock dataset is curated with the Yahoo Finance API We ascertain that abide to the Yahoo Developer API ToS".
H SOCIETAL IMPACT AND LIMITATIONS
Rational decision making under uncertainty has been extensively studied across many disciplines, but remains elusive even for humans. Our work bears significant societal consequences we live in a world where humans increasingly rely on intelligent assistants for diverse problems. Unfortu- nately, existing foundation models and LLMs are not capable of acting optimally under uncertainties yet, which may induce harm if the general public delegate these models for decision making. These risks exacerbated when intelligent assistants operate parency. DeLLMa serves as a first step towards an approach that builds trust for humans to rely on these systems to make important decisions under uncertainty.
black box without adequate trans-
While our approach sensibly improves the performance of LLMs for decision making under uncer- tainty, deploying prototype system in the wild without additional guardrail could incur significant financial losses, in case of suboptimal decision. We expect users to conduct extensive field tests for the feasibility of such systems, or leverage hybrid approach that integrates analyst judgements for optimal decision making.
Another limitation our approach stems from constrained state and action spaces. Our framework currently operates on small number of discrete state and action spaces, due to the context window
'GPT-4 ToS, Claude-3 ToS, and Gemini 1.5 ToS.
"https: /wwww usda www.uusd.....e....... www.gpolociiiseeooooooo and- 1inks 8 Fainacee yahoo. com
Yahoo Finance API ToS
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size of state-of-the-ard art LLMs. This drawback may potentially limit the applicability of our system in more complex use cases, or scenarios that require sequential decision making.
Finally, our current experiments focus specific domains (agriculture and stocks) representative, controlled environments for testing decision decision-making under uncertainty. Future work will involve evaluating DeLLMa across broader set of domains to better understand its strengths and limitations diverse, real-world applications.
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EXAMPLE SUMMARIZATION PROMPT FOR AGRICULTURE
agriculture published brevityo"
USDA:
Regort, Picare weite 3 cstai.cd summary of the repoct. You should tormat your response as JSCN object. The JSON ongect sheu.c contain the tolloxing keye:
"surnary'
in dstail, the everviev
price, yiold, prodection, what to plant, You should also incluce key ractors, such as veather, supply chain, and demand, that affect the mazket.
other incomeation relevant to tarmer making decisions about
deseribes, intornation apple prices production, "avocado"; a string that diseribes, in dstail, information pertaining to avocado in tac repezt. You chould include intormation avocado prices and promuttoon, a5 well as sactors tast actect then. infernation pertaining factors that affect
internation pertaining apple
report,
'grape";
sheuld include infermation en grape prices and production, as "grapetruse"; a string that deseribes, in detail, intormation pertaining to grapetruiz in the report. You should include information on gravefruit prices and producticn, as well as factors that affect
steing describes, setail, iatormation pertaining abould includa infermation en lemor prices and production, as well as factors that affect than. "peact": a steing that describes, detail, iafermation pertaining to peach in the report. You sheald include information
report.
production, "pear"; string that describes, dstail, information partaining pear in repost. You should includa infermation on pear prices and preductica, as will as fasters that affect then.
affest
"aacoraa'
of strings that
the factora that affect
the
report, You should Include at least S factors, sanked in decreasing ordes of Importaace.
Figure 8: EXAMPLE SUMMARIZATION PROMPT FOR AGRICULTURE
httpps:/www. ers usda gov/publ i cat cns/pub-detaa 1 1 //ppubi d- 107539
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EXAMPLE ZERO-SHOT PROMPT FOR AGRICULTURE
Below agricultore
overviex truit pertaining apgle, Narket Overviow: the USDA report indicates a general incresse in U.S. preduction of najor nencitrus cruits for 2021, with apples, grapes, peaches, cranberries, and eweet and tart cherries seeing weather groxer
publisted USDA.
Unised
epeats calitornia's engeing drought on crop yielde uncertain. price indices remein high, with Ficeruations throughout 2021. the concuner price index for fresh truit alse increased, suggestiac nigher retall prices. the northrest heat dane bas fatrocuced uncertainty, particulatly for except calisornia tangerines, forecasted to be dem frem the previous year's record, with smallar simons and sclact crops expected to increase grower prices. factore sush as weather conditions, supply chain issues, ard conand are inficancing the Apple:
citrus truits. prices higher,
with seciines supplies
Product Summary: apple pronuction is torseasted to ze up percert from 2020/21 but down 5 percent from 20:5/20. vashiactor. atazefs erop expected to ba larqer, but there is concern narkets potertinlly pushing nore apples into rise das to daclines in new york and michigan, which sccount for sionificant porticn of processed appios.
tarifts lowering prices. processing prices may
chalicngss,
California Price and Yield Statistica: average yield 15,000 average price paz
the
Avocado:
Product Summary: california avocado production has decreased, with kildfizea and water restrictions impacting yields. however, U.S.
consungtion incroased signicicantly, wita imports from mexico pera groving substantially. mexico sominates the avocado market, with importa peaking from, may through july, paruvian inporta competa during the sames nonchs, traditionally seriod of lever mexican imports.
California Frice 2,430 TON.
Yiold Statictics:
yield is 2.07
AERE and
average price
I'm z farner in Colifernia planning whaz fruit to plant next year. I would like to maxinize my profit with "10' aczea of land.
the Action 1. appla:
Action 2. avorado: 10 accea
would like which action abould based information provided
Figure 9: EXAMPLE ZERO-SHOT PROMPT FOR AGRICULTURE
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EXAMPLE CHAIN-OT-THOUGHT PROMPT FOR AGRICULTURE
PRONPT
egri
report publ by
gives
narket in the Uniced Stales, Narset overview: the usda report indicates general incresse in u.5. production of nagor noncitrus Fruits for 2021, with apples, grages, peaches, cranberries, and rise production, whsic pear production torecasted. deciane, the impact extreme weather events and calitornie's engoing drought on crop ylelds uncertain. truit and tree aut grower price ces renein high, with flucmsations throughout 2021 the consuner price irdex for fresh also increased, seggestiag production uncertainty, particulatly all commriitin except call corecasted to be dewn exon the previous year'e record, with snaller simons and salact crops expected weather conditions, supply chain
an additional focus en infornel pertaining apple, avocado.
and tart cherriex seelng
northrest hest U.d. tangerines, citrus prices are hi gighe, tree not suppl are
iatrocuced declines
prices. factors infleencing the mazket. Apple:
increase
cenand
Product Summary: apple product ion is forecasced to be persert from 2020/21 but down 20:5/20. vashiactor. over sanage, export narkets may remain sluggina potertially pushing nore apples into the donest narket and lowening prices, ces, processing ng prices may rise duc to dzclines in nои york and nichigan, which account for significant portica of processed
there high tarsits and cnipping canlienges,
Statistios: averzge price per unit is 0.240 / 18.
average apple
Avocador
Product
california avocado production has decreased,
wildrives
restrictions impacting yields. hovever, u.s. evocado consumption bas increased signiticancly, wita inports trom nexico and peru growing substentially. nexico doninates the u.d, avocado macket, with imports peaking from nay thzough july. paruvian imports competa curing the sumee moachs, traditionsily
mexican Statietics:
Calitornia avarzge price pez unit is 2,4 / TON.
average
yield
I'm tarner in Califernia planning what truit to plant next year, I would like maxinize land.
profit '10'
acticas Action 1. apple: 10 acres
BeloN aze
take:
Action 2. avocado: 10 acres
First think
unknow
would
final dzcisicns.
CONTEXT>
Now have enunerated the unknown factors that would affec ny final decisiins: <RE3FCHSE FROM FROME? CONTAINING LIM ENUMERATED UESONN FACTOR3> think
possiblity
would within
<SAME CONTEXT>
Now have cnuncrated the unknown factors that vould affect my final decisions:
<RESEONSE FROM FROMET CONTAINING LIM ENUMERnIED UNzONN FACTORSS erpirically
possibility lity
<RESFONSE FROM EROMPT CONTAINING IIM BELIEP DISTRIBUTION OVER UNzONI ENCTORS>
Given these unknow factors and the possibility estimates of these factore' ocourrences, think about final decision.
I would like to know which action should take based on the information provided above.
Figure 10: EXAMPLE CHAIN-OF-THOUGHT PROMPT AGRICULTURE
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EXAMPLE BELIEF STATE ELICIATION PROMPT FOR AGRICULTURE
Below agrizultore repert published by the USDA.
overviex fruit and nacket ln the dnived flalss, with an additional focus en infermation percaining to apple, avocado, grape, geapefrult, lemon, peach, pear, Narket Overvizw: the upda report indicates a general increase in u.s. production of major concitrus fruits 2025,
cherries seeing apples, grapea, peaches, cradbers daclins. the impact extrame veather cise production, whila pear production forecasted events and californie's engeing drought on zrop yields is uncertaia. fruit and tree act grover price indiows rensin high, with ians throughout 2021. the consuner price index for fresh fruit also increased, suggestiae higher retail pricas. the northrest heat producsion uncertainiy, perticalarly for trew fruita. the u.s. eatrus season ended with declines all comneditles except california Langerines, and citrus prices are highet. tiee ast suppliea are corocasted to be doan fxen the previous year's record, with snallar aimond and selact crops expsetad cunditions, supply chain issues,
has iatroducsd
increaze grover prices. factors influencing the masket. Apple:
denand
Produci Sunnarys apple prodact ion is forecasted to be peroent from 2020/21 but doen percent 2015/20. vashinctor atazefa erop expected be larger, but there is e'alllnges, over heat danage. export narkets a ney renain slogg in due to high tarl ffx and shippingg chall enges, sutentially pushing nore apples lnko the dumest in nacket and lowering pelces. processing pr grisss may cise due to daclines in new york and nichigan, which account for significant portica of processed apples.
California Price and Yiuld Biatistium: the average apple yield is 15,000 ACRE and the average price per unit in 0.246 $ 18.
avocado:
Pesddui Sunnesys caliiusnia avonado product has decreased, with wildfiows and
restrictions impacting yields. however, u.s. evocado consurpt son has increased signitioan:ly with imports from nexion and peru groking subatantiaaly with importa peaking Iron
mexice doninates the a.s, avocado market, thzough july. peruvian inporta compete during the sumee haa, imports.
traditionelly period
Californie Price and Yield itat the average avocado yield is 2.87 TONS ACRE and the average price per unit is 2,0 $ / TON.
pear:
Product Summasys pear production ion is foresanted to be similas the previous year, with losses in xashington but gains in oregon and call coliiarria, the inpact of the northwest heat wave product lan levels is still mosttain,, but Araditional pear trees with large canppies may offer some protection trom hest danage,
Calliforii Price and Yield Statistios: the average pear yield is 15.6 TONS / ACRE and the average price per unit is 565 $ TON.
would like to adept decision making under uncertainty transwork make decision, The goal you, decis naker, ankiown stale. The first step this proveduce suture state. The state in vector of 16 elements, each of which is randon variable, The state variebles are anunerated below:
opt inal ion, whille accouncing ing uncertairty
you to producw bellef diat over the
cllmmte corditions the climate condition the next agrictiteral sesson Caliternia supply chain cisruptions: the supply chain disruptions of the next agriculturel seasen in Ca1 Celiforria
applee price change: change in price unit apple
agrienltoral
Calitornia
agple yield changes the change in yield of apple for the next agricultural sesson in Califarnia -avocado price change: the caznge in price per unit et avocado for the next agricultural season
California
-voccaoo yiold change: the chenge in yield avocade for the next agricoltural season
Calitornia
change yiold of pear for You chocld rosnat your respanse as J50N objest with keys, wherein each key should state veriable from the Hsst above.
pear yield change:
acricuitural season California
seca key should map to a JSCN object with 3 keys, each of which is a string that descrites the value likely values ot the variable. Together, there keys should enumerate the natural lanquage. If the state staze variabla. Each key should naр your belief verbalized verisble is continuous le.g. changes to a quantity), you should discretize it into biar.
the
You should strict.y choose your beliet tron the tollowing list: "very likely', "Iskely', 'somexatt likely', "sonawhat unlikaly", 'unlikely', is "climate concition', and the top 3 most lskely valees are 'srought', "heavy procipitazion', and "enowstoen', then your response saouid be tornatted as tollows:
malikely'. For examoic, it the state variable
"elinate Lunn:: {
"dzoughe": " "sonexhat likely", "heevy precipitation *very likely", "snowelozn"" "anlikely" 1,
Figure 11: EXAMPLE BELIEF STATE ELICIATION PROMPT FOR AGRICULTURE
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EXAMPLE FAILURE CASE FOR AGRICULTURE
Ground Truth: Apple
Proupt
cnitted brevity)
apple:
Product Summary: apple prosuction is torecasced to be persent from 2020/21 but down 5 percent fron 20: 9/20. washingtor stazefa
expected be larger, but there is oper hest danage. export narkets may renain eluggisa due to high tariies and chipping chalicnges, potentially pushing nore apples into the donestic nacket and lowering prices, processing prices may rise duc to diclines in now york and nichigan, which zecount for significant portica of processed
Statistics: avarzge price per unit is 0.246 / 18. grapetruit:
Calitornia Price and
average apple yield 18,000
uri czop to half the volume predisted. florida cantinues to it grapetrust production, but the impact at citres greening disezse and decreazed beacing acreage has led to lower production levels. with reduced donestic production, importa have increased, and the average prica trapetruit
Prodect Sommay: grapetruit production has rignitienntly atfected by winter toxas, seducing
Calitornia
and
Statietics:
average grapetecit yield
the
avarage price per unit is 24.33 / B0x, oN TREE aQUIV. I'm z farner in Califernia planning wtat truit to plant next year. I would like maxinize my protit with '10" BeloN ase the acticns Action 1. appic: 10 scres
land.
take:
Action 2, grapecruit: 10 acres
Prediction: Grapefruit
on the previded data, grapstruit yielde highez revenue per scre apples aalifornia, the average yleld for apples is 15,000 ib / acre at price of 0.244 $ 1b, resulting in revenus of 636 per acre (18,000 ib 0.246 / 1b) in cantrast, grapetreit has an avezage 26.33
Response
results ia by qrapefruis production, such as the impact of winter storn uri and citres gresning disease, the hicher price and yield per scre nake grapetruit the more protitable cption for neximizing protit your 10 acres ez land califarnis.
equs, 115,115.71 pez acre 1457 boxes 24.33 6 box). - despite the shallenges
DeLIMa explanation
ranking following state-action pair
top
State climaie conditioa: inald deoughic supply clain disruptions: nodezale diss ionn,, apple prics change: increase, appla yield change: no change, grapefrui: price change: increase, grapefruit yisld
increzse
Action apple: 10 acres
Explanation given the information provided, prioritize acanarios sheze the price of the erop
is likely to increase and the yield ie not expeated to decrease signitisantlyy. state-action pair chosen the optimal action because it occure under continced drought (which is very Likely), with moderate supply chein disruptions (likely), an increase apple price (sonewhet likely), no increase gespofruit yseld (unlikely). this sugcests that
change apple yield (Likely), apples have a stable yield and a potential for price increase, making then a nore relithle choice for maxinising profit. the canking the other iction pairs based sinilas evaluation of Lhe expected out cunes, with prefecence fus scsnacios with atable or increasing prices and yields, Lower sisk et negative inpacts trom climate and supply chein dierupticns.
Figure 12: FAILURE EXAMPLE APPLE VS. GRAPEFRUIT
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EXAMPLE DELLMA RANKING PROMPT FOR AGRICULTURE
Below agricultore report publisted the USDA, It additional focus informtiio pertaining apple, avocado.
overviex truit and
Unised States, Narket Overviow: the upda report indicates a general incresse in 3.5. production of major noncitrus cruits for 2023, with apples, grapes, peaches, cranterries, and eweet and tart cherries seeing a weather
pear production. foreszated decling, the evcats and calitornia's eageing drought on crop yielde uncertain. truit and act groxer price indices remein high, with ticertationss throughout 2021, the consuner price index for fresh truit also increased, suggestiac higher retail prices. the northrest heat dane has iatrocuced production uncertainty, porticulatly for all comodiitis except california tangerines, forecasted to be dewn frem the previous year's record, with smaller simond and selact crops expected to increase grower prices. factore such as weather conditions, supply chain issues, and conand are influencing the merket, apple:
rise production,
truits. the citrus prices
sitrus
ended wita deciines higher, tree net supplies
Product Summary: apple pronuction is torseasted to ze up percert from 2020/21 but down 5 percent from 20:5/20. vashlactor. staze's erop is expected to ba larqer, but there is concern danage, export narkets my remain sluggien potertinlly pushing nore apples into the domestic nacket and lowering prices, processing prices may rise das to daclines in new york and michigan, which sccount for sionificant porticn of processed applcs.
high tarifts and shipping chalicages,
Calitornia Price and Yield Statistics: the average apple yield is 18,000 ACRE and the average price pez unit is 0.246 / 1B.
avocado:
Product Sumnazy: callfornia avocado production has decreased, with kildfizea and water restrictions impacting yields. hovevee,
avecado consurption has increased sienificancly, wita imports from nexico and peru groving substantially. mexico sominates the u.c. avocado masket, with importa peaking from, nay through july, paruvian importa competa during the samer moncha, traditionally seriod of lever mexican imports.
California Price and Yiold Statictics: the
avocado yield is 2.07 TONS MERE and
average price pez unit 2,430 / TON.
I'm a farnor in Califernia planning what fruit to plant next year. I sould like to maxinize my profit with '10' acces of land.
Below the
take: Action 1. apple: 10
Action avecado: 10
would like to adept decision making under uncertainty fsanswork make my decision. The goel of you, the decision neker, is to chusse an optinal aut ion, while accoun ing for uncertainly in the unknown atate. Previcusly, you have already provided forecast of future state variables relevant planting docisicns. The
is veetor of 6 elements, stale variablea (and their nost probable values) are unaeraaed beloe: elimate condition: "centioued drouch:': "vary likely', 'mild inprovenanet: 'senautaz likely', 'xignifieant improvemntts; 'walikely'
of which
randon variable. The
sapply chain dissaptions: 'ninor disruptions's "aonewhat likely', 'moderate diaruptiona"; "Iikely', 'severa cisruptions': 'sonewhat unlikely"
apple price change: "imcress': 'saneahiat likely', 'he thange'; "likely', 'decrease"; Ssomwhal anlikely"
apple yisid change: 'incroase'; 'somewhat uniskely", fao change': fiikely', 'decrease";
'sonexhal likely'
avocade price change: "Increase': fiikely', 'no change': 'scosshat likely', 'decrease';
"unlikely'
yield change: 'incrwsse' funlikely', change's 'sumeuhal likely', 1 'deccmase'
"Iikely"
telow, I have sampled set of state-sction pairs, wherein states are sampled from the state bel ie" disiriuutinn provided and iona are sampled unifornly from the action space. sould like Lo stilisy function
your comparisons
atate-action pairs
State Act Pair f, State: clinete conditionn contimued drought, supply chain disrupt ions: minor diaruption apple price charge: no change, asple yield changes no change, avouado price change: increase, avecado yield change: decrease; Action 1. apple: 10 acres
clinate condition: signi improvenent, supply chain disruptions: nodezace distuptiona, apple price change: decrease, apple yield change: decrease, avocado price change: decresse, avocado yield change: decrease; Antion 2. avocado: 10 scres [State Act Pairs 3- 3, cnitted for brevity]
State
Pair
State- Pals
Stale: clinate candition: continued drought, supply thain diss ipioe:
ninor disruptions apple price charge: decrease, spple yield change: increase, avocado price change: no change, avonado yield charge: increase; Action 2. avacado: 10 aures
You should fornal your responde as 1908 db dejtct. The JSCN jbject. should contain the folloving keya: decisiinn string that describes
to take. The ousput fornat should be the same as the format of the state assion pairs listed aboow, e.g- State-Action Fair 5,
state-action pair you recomend
rark: list of integera that rance the state-act ion pairs in decreasing rank preference. For example, if you think the first is the second nost preterred, and so on. For example, [I, 2, 3, 4, 5.,
act ion pair the nost preferced, the second state ion pair
explanatioon string that describes, in detail, the reasaning behind your deciston, You should include ion on the expected yleld and price of each fruit, as well as factors that affect then.
Figure 13: EXAMPLE DELLMA RANKING PROMPT FOR AGRICULTURE
26

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Published as a conference paper at ICLR 2025
FORMATTED GPT-4 SUNMARIZATION RESPONSE
indicates general increase "curnary"; *the apples, grapes, peaches,
protuction major norcitrus seeing
Fruits rise in production, whils pear production is torestates decline. the impact of extrame veathor events and calitornia's engoing srought on srop ylelds is uncertain. truit and tree nut grower with ficetsations threachout increased, suggestiag aigher setail prices. production uncertainty, particularly for tree cruits. the B.d. pitrue sesson ended wita declines in all commoditiss excapt california tangerines, and citrus prices are higher. tree act supplics are smaller simons previous year'e conditions, supply
fresh has iatrocuced
northwest
expected
grover prices, influencing the market.", "ayple";
preduction forecasted vashington state's ezop expested demage. export markets may remaia siuggish dus to high toritts and shipping challerges, porencially bushing moce apples into the demastic mazket and loweriar pricas. processing prices rise due to shich "yield": "19,000 ACRE", "price": "0.244 13
"sunnary":
2020/21 dowc largez, there concern heat
up
deciines
for signidicant partion
appics.",
"avocado";
"cunnary"; "salifernia avocado production decreased, vildfires vatez restrictions impacting yiclds. hovever, u.s. svocado consurption has increased sienifican:ly, with importa from maxico and croking substantially. nexico doninates the u.s. avocado mazket, through
imperts
monchs, imports curing mexican imports.", "yield"; "2.87 2ON3 / ACRE", "price": 42, A IOR"
traditionally perios
"crape":
"curnary": *grage predection is forecasted to be up 9 pereeat from 2020, despite drouch: and Seal conditiona california Lable grape production is also expected incrmase. ligh heal affected industrv, imports from mexice,
coachella shisneata slightly, but overall shipments were higher
central california shipnents
main competitor,
2021 than the previous year.",
"yleld": "6.92 7O53 MCRE" "prico": "908
"Grapafruit"; (...Onitted for Brevicy....
"lemon"; In..Cnisted for Brevity...ly "peach": I...Cnitted Brevity....,
"peaz"; (...Omitted Brevity....l
Patiraniecsy" I...Onitted for hrevicy...h.
"factors":
climate coaditions", *supply chain disruption iona slipping langes",""
*doneatie and inteanational danand for produce", "pest and disease pressures, auch cierue greening", and health Lrenda"
"changes
Figure 14: FORMATTED GPT-4 SUMMARIZATION RESPONSE
27

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Published as a conference paper at ICLR 2025
EXAMPLE CHANN-O--THOUGHT RESPONSE FOR AGRICULTURE
RESTONSE PROMPT
"response";
"unknown: faciora":
"veather conditicne": *the report mentions the ongoing drought in calitornia and tae inpact of the northwest heat dome
fruits. fature weather events such beatwavet, droughts,, signizicantly attect crop ylelds quality.",
"narket denand flactuations"; *coneuner preserences and narket demand for apples and avocados can change, inpacting the potential profitab! lity of each crop.", "supply chain issues"; "oteruptions
supply chain atfect ability selling prices.",
prodecte to etsicientlly "pest and Blsease outhreshs"; fom:rresks of pesta diseases eould reduce yields lead to increased costs for pest control and management.""
incluence
impact "Isbor availabi production, especially for lator-intensive zrops truits.", .n.nnited For Brevity...
costs*: "availability and of labor signicicantly
"sospease":
"giobal trade dynanico";
"improved trade agreements*; "scnexhat likely",
"increased tariffs": "unlikely",
"increased internat curpelition". "likely"
"1 liboo syallability and costst;
aviilabllitf:: "iikely", finersssed coals"; "very likely", "stable costa availabilisy": sonaahat unlikely"
nackel Bemand fluct inns""
denand for and avooados"; "likely", "stable denand agples and avocedoe": feoneshat likely", "decreased denand for apples and avocadon": funlikely"
ppatt
cumbreaks":
Poutbreaka leading yield loaat: "aonehhat likely", "ninor outbreaks with nanacsable inpact": "Iikely", Rao significent outbreaks"; "soneubas unlikely"
Cnitted Brezity...
RESPONSE To PROMPY 3
"decicion"; "action avocado: scres", *explanation"s "sased on the proviced data and the estinsted likelshood various factore affecting srop production, plaating avocados aspears to the moze profitable option. although chain issues,
avecasos subject ricks laber lity,
Figure 15: EXAMPLE CHAIN-OF- THOUGHT RESPONSE FOR AGRICULTURE
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Published as a conference paper at ICLR 2025
EXAMPLE DELLMA RESPONSE FOR AGRICULTURE
RESPONSE to BELIET DESTSTBUTION ELZOSTADION
"climase condition";
Lnued drought"; "vecy likely', "nild improvenent"; "sonevhat likely", "signicicant improvement": "enlikely"
" Suppyy chain dissuptions":
"ninos diarupt lums*; "somewhat Likely", "noderata diaruptions"; "Lixely",
"coveze discupticna": "senswtat uniikely"
apple price chanos":
"sonawhat 1ikely",
Rao change"; "Likely",
"docrsasa": *sonawhat unlikely"
"apple yield change":
"increasa"; "sonawhat unlikely",
Rno change": "ikely",,
"decrsase"; "sunwhaat likely"
Pavorado price change":
"increase"; *1-kely*,
"ho change": "somexhat likely",
"decrease"; *unlikely"
aavorado yield change";
"incrmaze"; funl inlli"
"ao change?; "aoneshat likely", "decrease"; "Iikely"
1, ..iittedd For Brevity...
RESPONSE TO
"docisisn": "statc-action poir 18", "rank":
28,
8,
22, 23,
20, 14, "oxplanation"; "che decision-mmkira process under uncestainty involves evalusting the potentinl outcones at disterent actiors given the probaale states ot the world. tais case, various factors bosh rruits.
25, 21,
considaring planting apples avocades california,
suca climate conditions, supply chain disruptions, and changes in pisid price the geal is to naximize protit on I0-acre tarm. to rank the state-action paits, we coneider the most probable values of the stata variables and their impact on the autcones of eact: action. the action recommended
stility sunction ccastructed state, coneidering sota yseld and price changes. state-action pair ropresents a scenario with continaed drought and nodorate supply chain disruptions, waich are likely to attect both crops. hevever, the price and yield of avocados are expected to increace, which suagests higher does not increase. price and yield conditions, especislly given the aigaer average price per unit for avocados and the significant increase in u.a. consumption, which sugreata strong nazket danand. scenarios disruptions decreases potentisl for 105s."
expected profitabi
given
price remains unchanged ranking reflects preference for scenarias where avecados have favozable
profit compared
yield
price ranked lower the higher risk
Figure 16: EXAMPLE DELLMA RESPONSE FOR AGRICULTURE
29

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Published as a conference paper at ICLR 2025
EXAMPLE FAILURE CASE FOR AGRICULTURE
Ground Truth: Avocado
Proupt
cnitted brevity)
avocado:
Product Summery: calicornia avocado production has decressed, with wildtizes and xater restricttions inpact yields. bowever,
evocado consurpt has increased signi fioen:y.. Mita imports from mexico and peru groving substantially. nexico soninates the u.c. avocado nazket, with inports peaking tron may through july, peruvian inports conpete curing the sumner monchs, traditionally period of lever mexican imports.
California Price and Yield Statistics:
avocado yield is 2.07 TONS MEEE and
average price per unit 2,430 grzpe:
TON.
Product Summery: g:ape prosuction is torscasted to ze persent from 2020, deepite drought and heat conditions. californis tab eolee-ype grape produstion ion is also expected to incresse, high heat des affected the industry, with cozchelia valley thigneats sovn central colifornia shipncacs up. imports from mexice, the main competitor, were down slightly, put overal. shippents were higher in 2021 than the pravious year.
ylold 5.92 TONS ACRE and the I'm a farnar in Califernia plannine what fruit to plant next year. I would like to maxinize my protit with "10" acres or land.
Colifornia Price and Yiold Statictics: average price per unit
5 TON.
Belox the actions Action 1. avacado: 10 acres
take:
Action 2. grape: 10 scres
Prediction: Grape Response
the sienificantly higher yield grapes per acze makes grape productior nore protitable pez-aare basis. cocitionally, she grape production is forecasted to be up deapi:e the droucht and beat conditions, indicating a reailient crop under cuzrent conditions. in contrast, avocado production has decreased due to wildrires and restrictions, which naxinize profis an the acrea of land.
aitheugh avocados
higher
higher
taerefors, planting grapes is recomsensed
DeLLMa explanation for ranking the following state-action pair at the top
State climate conditicn: continued drought, supply chair. disruptions: ninor disruptions, avocado increase, avecado viald change: decresse, grapa
increase, grape yield
chaago: no change
Action avocado: 10 aссия
Explanation the decision based the conkination of favorable price changes for avocados and increase In avocade yield despita the continued drought. the state indicates an increase in avocado price,
in avocado yield, which sonexhet Likely. hovever, the decrease in yield is offeet by the high price per bon for avocados, which is significertly higher than grapes. additionally, the state indicates increase grape price, waich is somewhat likely, and no change in graps yield, which is somekazt likely. howIver, the lever price per con for grapes makes the avocado opt lan, noce appealing finamcially. the inved drought condition is cunceza for both erops, but avacados have historically been to their higher price per unit and strong narket demans. the minor supp-y chein dieruptions are less severe than moderate cieruptions, which could attect grape mazket due to its reliance timely chiprents for tresh consurption, the conbination at high prices and the ability to withetand dreucht conditions makes avecados
likely, and
pcuficable crop in califurnia duw
optimal choics for maxinizing profit the 10 acres land
available.
Figure 17: FAILURE EXAMPLE AVOCADO VS. GRAPE
30

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Published as a conference paper at ICLR 2025
EXAMPLE FAILURE CASE FOR AGRICULTURE
Ground Truth: Pear
Proupt
cnitted brevity)
lemon:
Product Summary: Lenon promaction is st its .ceest in tive years, with 6 percent decrease For the fresh narket. both asli and arizona smiller crope, with arizgna experiencing a signiticant drop dos to heat danage. despite lover donestic production, tresh lenca imports increased, and U.S. tresh lemon exports decreased. grower prices tor lemons have risen by pereent.
California Price and Mield Statictics: average lemon yiold 428 BOXES ACRE and the average price per unit 23.3 BOX, ON EQUTY, pezz:
Product Summery: pear production. is torecastes to be cinilar the previous year, Nith losses in kashingten on production levols is still uncertain, but traditional pear tress with large canopies may ofter some protestion trem hest danage,
gains oregon california, the impact
northwest heat
California Price and Yield Statistica: the average pear yield is 15.6 TONS / ACRE and the TON.
price
tarner in Caliternia plaacing what trust to plant next year, I would like mexinize my erofit with '10' aczes of land.
BeloN are the acticns can take:
Action Lemon:
Action 2. pear: acxes
Prediction: Lemon
Response
colculate expected revenue for both Lenons and per and ther extrapelate that to acres. fox icmons, the average yield is 428 boxes per acse, and the average price per box is $23.3. this resulta in an expected ravenue of 428 boxes/acze x 322, " 93, 972.4 pez aire. for 10 acros, this would be 699, 724. for pears, the average yield is 15.6 per
based
provided
and the Lons/acze 8565/son 88,814 poz acre. for 10 acres, this would be 140, comparing lcmons are expected to yield hicher reverac pez acre than pears. acditionslly, the repert indicstas that lemon production is at its lowest in rive years and grower prices have riden by percent, shich could patentially lead pear production is forocasted be similar the previous year and the impact save da production levela is still unceraain, therelure, baaed on the expected revenae and nackst conditions, planting lenens vould be the more profitable action. DeLIMa explanation
is 9565.
results
expected
of 15.6 twa,
if the
continues.
the other hand,
northwest heat
ranking the following state-action pair the State clinate conditiear sild lagrovenett, aupply chain discuplione: aLnox diaruptiora, lemon price chanre: dacreasa, lemon yield charge: no change, pear price change: increase, peaz yield change: docrease
Action peas: 10
Explanation the state indicatea a mild Lngrovenent in clinate conditions, which is bensficial for crop growth, ninor supply chair disruptions, which are less likely to inpact the delivery and sele of the produce. additionally, the lenon price is expected to decrease, and the lenor yield is not expected to shangs, which could result lover prefitability for loncns. on the other hand, the pooz price is expected to increase, and although the pear yield is expected to decrcase, the aighor price sould compensate for the lover yisld, potcatially leading to higher profits for pears. Our Lleenee,, the everage yield and price unit for peace in calnio are favorable, with high price pes Lon and reasonable yield per acce. given the unsestain lew in the nacket and the specitic concitions ot the state variables, planting pears to this scenatio, paire with conditions that are nore likely to lead to increased protitability and risk ranked higher.
prudent choice in
Figure 18: FAILURE EXAMPLE LEMON vs. PEAR
31

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Published as a conference paper at ICLR 2025
EXAMPLE ZERO-SHOT PROMPT FOR STOCKS
Below
considering: ADD, GS. would like which should only buy
historical prices I have a budgst or 10000 dollars. would 1ike to maximize ny prstit. Teday is 2023-12-01. I'n auying stocks today and will sell then at the end of the month 12023-12-25). informatio
information
Below
Devizes). Unita dollars
share.
Corrent Price: 1.9,80.
aistorical Prices: 2021-12: 143.49, 2022-01: 126.34, 2022-02: 119.63, 2022-03: 112.68, 68, 2022-04:
95,80, 2022-05:
2022-06:
90.85, 2022-07: 02.39, 2022-08: 96.33, 2022-09: 14,95, 2022-10: 2022-12:
2022-11:
2023-03:
90.81, 2023-05: 102.22, 2023-96:
117.79, 2023-07:
113.68, 2023-08:
108.82, 2023-03: 103.11,
2023-10: 192,56, 2023-11: 117.39.
informatin
11.0. GaneStop
Unita
dollars
corrent
atsooical Prices: 2021-12: 39,38, 2022-01: 29,49, 2022-02: 29.20, 2022-03: 29.93, 2022-04:
36.32, 2022-05:
26.37, 2022-06:
22.24, 2022-0T:
34.21, 2022-08: 60, 2022-09: 26.31, 2022-10:
25.85, 2022-11:
2012-12:
21.50, 2023-01:
19.69, 2023-02: 20.84, 2023-03:
19.42, 2023-04:
22.2%, 2023-05:
2023-06:
24.20, 2023-01:
23.06,
19.12, 2023-09: 17,06, 2023-19:
14.33, 2023-11: 12.15.
I'm trader planning ny next nove.
I would like naximize profit with 10000 dollare.
the actions
Action
dollars
Action 2. CHE: 10030 dollars
would like which
abould based infocmation
Figure 19: EXAMPLE ZERO-SHOT PROMPT FOR STOCKS
32

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Published as a conference paper at ICLR 2025
EXAMPLE CHAIN-OT-THOUGHT PROMPT FOR STOCKS
PRONPT
like prices last nonths. can only bay are stock and I have budget at 10000 dollare, would Like to maximize my protit, Teday is 2023-12-C1. I' buying stocks today and will sell then the the morth 12023-12-200 share.
conaidering: the information their
buy
intormation
(i...
Devices).
pez
Current Price: 199,88.
aistorical Prices: 2021-12: 143.49, 2022-01: 125,94, 2022-02; 119.63, 2022-03: 112. 98, 2022-04: 2022-05:
2022-08:
2022-09:
80,32, 2022-11:
69.61, 2022-12;
68.C9, 2023-01: 73.27, 2023-02; 82.07, 2023-03: 90.43, 2023-04:
90,81, 2023-05: 102.22, 2023-06:
127,95, 2023-07:
113.69, 2023-08:
108.82, 2023-03: 103.11,
2023-10: 56, 2023-11: 117.59.
the information
(t.c. Gemestop cocp).
per share.
corrent Price: 14.52.
Retoricaa Prices: 2021-12: 39,48, 2022-01: 29.49, 2022-02: 29.20, 2022-03: 29.93, 2022-04:
36,32, 2022-05: 26.57, 2022-06:
32.70, 2022-0T: 36.21, 2022-08: 35,60, 2022-09: 26.81, 2022-10:
25.85, 2022-11: 26,21, 2022-12:
21,26, 2023-01:
2023-02:
2023-03: 19.12, 2023-09:
21.27, 2023-05: 21.55, 2023-08:
24.38, 2023-07:
23.04, 2023-08: 19,12, 2023-09: 17.86, 2023-18:
14.33, 2023-11: 13.15.
planning
like naximize protit with 10000 dollare.
Action 1. AMD: 10030 dollars
Action 2. GME: 10030 dollars
First
the unknown
would affect final decisions.
CONTEXT>
Now have cnuncrsted the unknown factors that would effect ny final decisions:
<REFFCHSE FROM PROME? CONTAINING LIM EXUMERATED UNSONN FACTORS> think about possiblity
factor would within
<5AME CONTEXT>
New have anunerated the unknown factors that would d offect ny Final dedl sions:
<REaFCNSE FROM PROM? CONTATNING LIM EXUMERATED DNSOON FACTORS>
erpirically estinated possibility 11STAT ownn UNKOWN FACTORSA
paseible
CONTATNEMG 1IM Given these unknow factors and the possibility estimates of these factors' occurrences, think about final decision.
dAPSPOOES
would like know which action should take based on the information provided above.
Figure 20: EXAMPLE CHAIN-Or-THOUGHA PROMPT FOR STOCKS
33

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Published as a conference paper at ICLR 2025
EXAMPLE DELLMA RANKING PROMPT FOR STOCKS
which stook should bay based information their historical prices in the last 24 nonths. can only buy stock I have a budgst or 10000 dollars. I would like to maxinize ny prstit. Teday is 2023-12-01. P'n auying stocks today and will sell then at the end of the month 12023-(2-29) Below the informaiio share.
Below the stooks considering: AND, RS. I would like
AND (1.c. Advanced Hicro Devizes). Unita are in dollars
corrent Price: 1:9,80.
aistorical Prices: 2021-12: 143.49, 2022-01: 126.34, 2022-02: 119.63, 2022-03: 112.68, 2022-09:
90.e5, 2022-01: 02.99, 2022-08: 96,33, 2022-09: 14.95, 2022-10: 82, 2022-11: 69,61, 2022-12:
95,80, 2022-05: 91.27, 2022-06:
68.09, 2023-01: 19.21, 2023-02; 82.0%, 2023-03: 40,47, 2023-043
90.31, 2023-05: 102.22, 2023-06; 117.79, 2023-07: 113.69, 2023-08: 108.32, 2023-03: 103.11,
2023-10: 56, 2023-11: 117,39,
Below the information about
GME Hie. GeneStop Corp). Unite in dollars per stare.
current Price: 14.32.
atsooical Prices: 2021-12: 39,3e, 2022-01: 29,49, 2022-02: 29.20, 2022-03: 29.93, 2022-04:
36.32, 2022-05:
34.21, 2022-08: 20, 2022-09: 26.31, 2022-10: 21.50, 2023-01: 19.69, 2023-02: 20.84, 2023-03; 19.42, 2023-04: 22.2%, 2023-05: 21.55, 2023-06:
26.37, 2022-06:
32.74, 2022-07:
25.85, 2022-11: 26.21, 2012-12:
24.20, 2023-09: 23.06, 2023-08: 19,12, 2023-09: 17,06, 2023-19:
14.33, 2023-11: 13.15.
I'm tradzr planning ny next nove. Below the actiens
I would like to naximize profit with '10009' dollars.
take:
Action 1. MD:: 10030 dollars
Action 2. CHE: 10030 dollars
I would like to adopt decision naking under uncertainty franswork to make decislor. The goal you,
uncrttintty unkaown state. Previcusly, you have siready provided torscast at tuture state variables rolevant to planting dacisiens. The state is a vector of 20 elsments, each of which is sandon variable. The state variables land their nost probabic values) are caunerated below:
dacisien maker, is chosse optinal action, while accouncing
<ииоPRAAт STATE BEL EISTRIBUTION
Belox,
state belief diacribution you provided and act lons are xampled unifernly from the action space. would like to know which assteraction pais I should Lake based on the information you have so far.
sempled
state-action pairs, wherein states earpled
State-Action Pair 5, State: econonic health: recession, market sent ment and irvestor paychology: neutral, politiesl evens
governmett policies: stable, natural disesters and other "black avan' eventa: none, coopolitioal lasues: stable, nerges and najor acquisitions related to advanced niero devices land]: rone, regulatory cranges and legal lasues happened to acvarced nicro devicws cand): : minor, firancial health of advanced nicro devices land): veak, company growth ot advanced nicro cevices (and) : stagnant, company product launches
acvanced micro devices (and): moderate ingact, nerges and nagor scmpisition related to garestop corp Igne): none, regulatonn changes and legal issues hagpened to ga curp (gne): na najor, financial al health of ganestog curp company growth ganestop corp (gnel: stagnant, comрany product lsuncaes ganestop 10030 dallers
(grel:
corp (gne):
inpact;
State Action Palx 2, State: economic health: recession, market sentlment and investos payctology: optinistio, political evence and government policies: stable, natural dieasters and other 'black avan' eventas ninor impact, geopolitical issues: stable, nerges and najer aogur aoquisitions related to advansed niere devices
none, regulatosy thanges and legal isausa happened advanced nicro cevices land) rone, tinancial health et advanced nicro devices (and): stable, comaany growth of advanced micro devices (and); repid, company product "aunches of advanced nigro arquicitiens related to gamestop corp (gme):
devices (and) suczerstul, legal issues happened to ganestop corp (gnel: none, tinanciel health genestop corp (gre): : strong, comgany growch of ga corp (gnel: rapid, company producs launches ot gamestop carp igme): unsuccesstul) Action 2, eE: 16000 dollare
regalatory changes
State-Action Pair 50. State: econenic health: stable, market sentiment and investor psychology: uphesval, natural disasters and other 'black exan' evente: ninor ispact, geopolitieal issses: stable, nerges and nagor aequisitions related to advanced nicre devices (ane): najor, regul atory changes and legal sssees happened to advanced nicrs sevizes land): niaor, financial hezlth of advanced nicro devices (and): stable, company growth et advanced micro devices (and): oes
ncutral, political eveats governnens policies:
company prosuct Launches et advanced najor seguisitione related to garestop corp (gne): none, requlatory changes and loçal issues happened to ganestop coup (gnel: major, riatncial health at gareetop corp igme): veak, company growth at genestop corp ignel: moserate, compary prosuct lons): succasstul; Action i. AND: 10000 dollars
moderate ingees, nerges
isuaches of canestep
You should tornat your respense as J508 ostest. Tae JSCN object should contain the tollowing keys: decision: a string that describes the state-action pair you recomend the trader to take. The output fornat should be the sans the format of the statz-action pairs listed above, e.g. State-Action Fair
cark: list integers that rance the state-action pairs decreasing rank preterence, For example, if you think the first stste-action pair is the most prefersed, the second stste-sction pair is the second most preterred, and co on. For example, [I, 2, 3, 4, 5.,
explanation: string that describes, in detail, the zeasening behind your deciaion. You should include information ion on the expected price oz each stock, as well as factors that affect than.
Figure 21: EXAMPLE DELLMA RANKING PROMPT FOR STOCKS
34

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Published as a conference paper at ICLR 2025
EXAMPLE ZERO-SHOT RESPONSE FOR STOCK
"decision"; "ectionn
"oxplanatian"; "based on the aistorical price dats provides, and has shom pattern ot increasing in value tron november to cecenber in the previous year 120221, geing trom 69.61 in noventer 68.09
aithough this is sligat decreass, simpiticattiy higher taen november 2022, suggesting stzonger
is important
price november pertornsnce year over year, additionslly, andt 5 curtent price ot 119.88 is close to its zecent high in june 2023 of 127.75, indicating poteacial for continued ezowth scabllity the start cern. price
december ia 2322, previous year's december price 21,55, suggesting perfornines and potentially 15ss roon for growth witain the same tim:frenc. given the hictorical data and the curcent trende, lavesting in and sppears to have higher potential for prorit when december 2023.
gne'e historical
deciine
selling
it important
always indicative reture recuits, this enolysis dees account exterael factors such as nacket conditions, company pertornance, and industry trends that could impact stock prices."
Figure 22: ZERO-SHOT RESPONSE FOR THE STOCK DATASET
35

---

Published as a conference paper at ICLR 2025
EXAMPLE CHAIN-OT-THOUGHT RESPONSE FOR STOCK
RESTONSE PROMPT
"response";
"unknown Factora":
"narket conditions"; "ourrent nacket conditions and economic indicatore such as interest rates, inflation, and eogronic groxth
signifionntly impect stook prices,
"corpany pestormance": "cuture carnings reports, predect launches, corpany-specl nexs that could attect the stock price are unknom."
"industry trerds"; *changes in the techno bgy and retail sectors, respect ively, could infiuence pertermance "regulatory changes": *any spconing regoletory changes operat lons or industry dynemics are not know..",
gme.",
smgact companies'
"investor sentinens"; *the ovarall sentiment of iavestore towarde the stock mazket and these specitic stocks could ... For Brovicy...
affecting their prices.",
RESTONSE TO PROKeT 2
"reapcose";
"compay parformance";
"future sornings reports exceed expectatione:; "soncwiat likely",
"preduct launches aze succesaful and vall-reaeiied"s "likely", "corpany-spacific
*sanewtat unliksly"
compeccitinn"
"compstitors reicase superior products": "somextatt likely", "adikely", maintains compotitive edge": "likely"
"csepelitara significant
"global uvents*;
"ssopolitical tensiens escslate"; "sonawhat Lixoly", "natural disesters affect narket"; forlikely", foww pandenic arisus": "oway ualikely"
Linduatry tremds::
PLechnelogy awocos continaes Lo goox*: "ikely", "rotail sector faces challenges"; "sonawhat likely', "shift consuner behavier affecta gne*: "likel"
C..Cniteed ed Brezity...
RESPONSE TO PROMPY 3
"decision"; "ecticn and: 10300 collars",
"explanstion": "based on the nistorical prices provided and the enpirically estimated possibilities of varicus factors occurring, the recomncatation is to purchase and stock. aistorically, and's year (2022), going
increase novenber december in the previous per 838re..."
Figure 23: EXAMPLE CHAIN-OF-THOUGES RESPONSE FOR STOCK
36

---

Published as a conference paper at ICLR 2025
EXAMPLE DELLMA RESPONSE FOR STOCK
RESPONSE To BELIET DESTSTBUTION ELIOsTADION
"cconnicc health";
"gzovth"; ; "acnenhat likely",
"stable"; "Likely", "rocession": "scmonhat uniixely"
market sentiment and investoz psychology": [
"oplinistie"; "sonwvlia likely",
"asutral"; "likely",
"pessinistia": "sonewaat unlikely*
poliitical evants and goverament policies": [
"stabio": "Likely",
"ninos changes": 'sanwliat: likely",
"najor upheaval*: "unlikely"
and other "black wounta";
"hone"; "likely",
"minor ingass:; "someahat likely",
"najoz imgasi"; "unlikely"
"geopolitioal lasuest;
"stable"; "aunenhiat likely",
"censiens rising": "Likely",1ikely""
"escelat conflint": "anlikely"
Fnerges rges and major acguiaittions related to advanced micro devices (andi "
"none"; Every likel",
"ninoz": "sonawhas unlikely",
"nagor"s "unlikely"
Cuitced For Brevity...
RESPONSE TO
"rank": "statc-action poir 32", 12, "oxplanatia"; "che docision-makira process under uncertainty involves evalusting the state-action poics aased the proviced state variaales and their most probable valuet, the gool naximiza investment. given the budget constrain: to consider the historical pertormance of the stocks, the current nacket consitions, and the state varishles that could influenes the stocks' Zuture prices. and has shown stroad recovary irend pasitive cutlook the company,
"docision":
1. 18, 14, 23, 29, 27, 20, 30, 20,
15,
this and goal seil
morth, nood
the kita moderate rapic. growth and succesetul promect launches being sonewhat likely. gre, or the other hand, aspears zo be in waakar position with stagrast grouth and weaker finaacial health aeing more likely. the chosen stato-sction pair 32 suggeste optimestic narket sentiment, stable sccaonic health, and eveata, which could indicate highar risk but potentially hicher reward scenario. 'black awan' ranking provided orders the state-action pairs tron nost to least pretarred sased on the pocitive insieators for and and the negazive indicators for gme, with a preference for scenarios that acgges: acononic sucsessfel company stable epcinistic market seaziment,
variables
product launch for and,
for and.
Figure 24: EXAMPLE DELLMA RESPONSE FOR STOCK
37


Reviews:
[{"review_id": "4QpD4RwmHh", "reviewer": "Reviewer_RA2F", "summary": "The authors  propose  framework to work with LLMs for decision making under uncertainty; in doing so, they take classical utility/decision theoretic framework as a design guideline: (1) they enumerate first all the states. (2) estimate the probability of those states (3) elicit the utility function from the user, and (4) choose the one that maximises expected utility.   They show their results working with various LLM and in various settings; different preference ranking strategies (pair by pair vs. top1) against benchmarks of zero shot and CoT in two real world  decision problems (one in agriculture the other in stocks investment) and show that they their framework helps with the results significantly. Also considered are ablation studies (on  things such as overlap percentage (of minitibatches in drawing from state-action samples) and sample size, human agreement etc ). Approach also comes with the decision trees naturally, hence making the whole process more explainable.", "questions": "See the points above as \"(question)\". Happy to read your take.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Exposition and writing  in general is in  good condition. (There are small rooms for improvement in organisation and clarification.)\n\n- The whole framework is written with careful formal details, thanks to taking decision-theoretic frame, which is a great idea to start with.\n\n-  The results on improvements are powerful; also has high impact potential  due to popularity of prompt engineering in LLMs. The idea is novel and justifiable (with some inherent difficulties and pitfalls ).\n\n- The depth of the analysis and various ablation studies is a strength. \n\n- The framework also provides (to some extent) an explainable medium which is a strength.", "weaknesses": "- Content-wise: Unfortunately, too many reference to appendix in many crucial points. This is of course due to  tight page limitation, but authors should seriously think about re-arranging the text more complete. For instance explanations for Table 2 , 3 and 4 really not sufficient (page 10 only call their names without telling what they are).  It seems like the paper would serve much better as a journal publication or other venues with more page (e.g., ICML).  Related: Say also that Figure 11  is in appendix actually.  They try to provide too much (for instance human evaluation at best a study on its own, or should be at least extended to present something substantial)\n\n- On the technical side there are strong assumptions and unclarities:  the assumption of independence between k-latent factors is very strong, and  actually hard to maintain,  also due to natural language part of things.  As a result, uncertainty in the estimation step is taken very crisp, it is pretty difficult to ensure whether these scores are reflecting reality. There is a section called: \"How good are state forecasts\" (which is a good to have section), however I am not sure it reaches its goal:  This is inherently difficult. They show table 1: claiming two metrics they showing \"good results\" is hard to justify since we don't know reference to what (maybe its my misunderstanding.) Why are these reasonable? Also the same section, manually annotating a set of ground truth values, how to get it, etc, not very clear to me. \n\n- Less problematic is the issue of  the number of alternatives are known (coming from the context or imposed to LLM) is a strong working assumption, and undermines  real world settings applicability. \n\n-Another major issue is the part with Table 4 (human evaluation): this is a nice to have I agree, however, if you go for it then it should  be done in a state that does not weaken the paper. Currently, as explain this is just done by authors, of which we don't know how many, say 4 or 5 on average. Is this result than solid? It is problematic for two reasons: 1)  It should be done not by authors  but by a handful of people (who has nothing to do with paper acceptance) 2) not clear how does it compare to sole human comparison 3) amount of people matters: what if it was a single author paper (I assume it is not due to their expression). What is the standard deviation, significance level  or other statistics on these values? Without all these things , it would be pretty difficult to publish this in even a low-rank psychology journal. And yes, this venue is on rather AI but we still should maintain some sensible quality on experiments if we are to show that. Currently, it just serves only as good as an anecdote.  (Question)\n\n\n- It is shown to have much better performance against OpenAI's o1 model, but which DeLLma is used is not clear. (Question) \n\n-  Organisation wise  the DELLMa main picture (there is no name unfortunately for the figure btw to refer to it, but perhaps understandable) comes before the explanations in 3.1 and other sections and create questions immediately. This is a very minor issue. \n\n-Unclarities in  Page 9, Main results, second paragraph is not satisfying:  The vagueness of explanation is not a weakness per se since authors are careful to call these as hypotheses. Yet, the difference on phenomenon is very small, and now we enforce ourselves to explain the phenomenon, and likely we hallucinate ourselves.  The result can be much more simple: the amount of data considered, and the processing capacity of the model \"can be\" pretty much be  a reason that top 1, simplifies it much better compared to other.   Also see that when number of alternatives is 5 it already performs better, which might be due to a particular alternative. Relatedly the wayt the average is taken matters: 1) some difficulties might be due to assessing particular actions (are these scores are evaluated permuting through all the alternatives? (E.g., which 2 actions matters)?) 2) averaging across the accuracy score of  different size of scores matters. There I would go for higher weight to the higher number of actions.  These subtleties are unfortunately not clear, (question)\n\n-Mini typo: availble fruits.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "qbtFcFkffJ", "reviewer": "Reviewer_4x55", "summary": "This paper proposes DeLLMa (decision-making LLM assistant) to maximise the utility in decision problems, i.e., agriculture planning and finance. DeLLMa consists three steps, 1) based on in-context information, infer and forecast related unknown variables, 2) elicit the utility function which aligns with the user's goals, 3) use the elicited utility function to make the best decision which maximise the expected utility.   Authors claim that DeLLMa is motivated from chain of thoughts and tree of thoughts, but is faster at the inference time especially in the scalability.", "questions": "- How will DeLLMa perform on other tasks related to chain-of-thought or tree-of-thought methods discussed in related work?\n- If scalability is not a significant issue in certain cases, will DeLLMa outperform other methods?\n- In the experiments, one task involves investing on December 1, 2023, and selling on the last trading day of that month (December 29, 2023) to maximize returns. Why were these specific dates chosen? Why not other dates?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- Clearly explain the main concept of DeLLMa as illustrated in Figure 1.\n- Capture LLM in decision making with a triplet P=(G, A, C) and four steps, where G is the user goal inferred from the description, A is a list of actions, and C is the contextual information. \n- Include two ranking types: pairwise and top-1.\n- Provide clear prompts and code in the appendix.", "weaknesses": "- Inferring the user’s goal from the context is done by the LLM, which requires the user to have a clear goal in mind. For example, if the farmer has not decided to plant only one type of fruit, DeLLMa may not know how to handle an implicit multiple-fruit situation.\n- The applicable scope of DeLLMa is limited. The experiments include only agriculture and stocks, which may mislead readers into thinking DeLLMa generalises across all decision-making tasks.\n- There is little information about human agreement; the paper states that the authors served as human annotators, but details are missing.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "OSIEyvtnLM", "reviewer": "Reviewer_GCWw", "summary": "The paper introduces DeLLMa for better decision making by LLMs under uncertainty. It uses a multistep process to achieve this, based on decision theory - state enumeration, forecasting, then utility elicitation and expectation maximization. Then the paper applies this to agro and fin scenarios - showing good improvement over baselines (zero shot, CoT etc). Finally the paper shows that this method generalizes across LLMs and is human auditable.", "questions": "1. How can DeLLMa be adapted to handle continuous state and action spaces? \n2. The state forecasting method assumes independence bw latent factors. How does this impact total performance?\n3. Is DeLLMa able to handle cases where the user task cannot be distilled into a hard utility function?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "Originality - the paper is quite original, and the proposed pipeline is creative. I have not seen this multistep approach used with LLMs anywhere before.\n\nQuality - I found the method to be quite well motivated and well presented. The paper is reproducible, and the experiments seem cohesive and well-motivated.\n\nClarity - The paper is well articulated and clearly structured.\n\nSignificance - The paper is definitely an important step in the right direction. Uncertainity quantification in ML has been an active area of research and I am curious to see how this is furthered with LLMs. However I feel that the method has limited applicability to real world scenarios.", "weaknesses": "1. The context window of LLMs clearly limits the state and action spaces, which makes this method of limited real world applicability.\n2. It appears that the method is very sensitive to the first state forecasting step. While the method is robust in general, I would like to understand if this is indeed a big bottleneck.\n3. The utility elicitaiton method proposed (pairwise ranking) seems a bit too simple for real world scenarios. I would like to know if other methods were tried for this.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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:

Decision: Accept (Spotlight)