The Challenge of Using LLMs to Simulate Human Behavior: A Causal Inference Perspective

• URL: http://arxiv.org/abs/2312.15524v1
• Date: Sun, 24 Dec 2023 16:32:35 GMT
• Title: The Challenge of Using LLMs to Simulate Human Behavior: A Causal Inference Perspective
• Authors: George Gui, Olivier Toubia
• Abstract summary: Large Language Models (LLMs) have demonstrated impressive potential to simulate human behavior. We show that variations in the treatment included in the prompt can cause variations in unspecified confounding factors. We propose a theoretical framework suggesting this endogeneity issue generalizes to other contexts.
• Score: 0.32634122554913997
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have demonstrated impressive potential to simulate human behavior. Using a causal inference framework, we empirically and theoretically analyze the challenges of conducting LLM-simulated experiments, and explore potential solutions. In the context of demand estimation, we show that variations in the treatment included in the prompt (e.g., price of focal product) can cause variations in unspecified confounding factors (e.g., price of competitors, historical prices, outside temperature), introducing endogeneity and yielding implausibly flat demand curves. We propose a theoretical framework suggesting this endogeneity issue generalizes to other contexts and won't be fully resolved by merely improving the training data. Unlike real experiments where researchers assign pre-existing units across conditions, LLMs simulate units based on the entire prompt, which includes the description of the treatment. Therefore, due to associations in the training data, the characteristics of individuals and environments simulated by the LLM can be affected by the treatment assignment. We explore two potential solutions. The first specifies all contextual variables that affect both treatment and outcome, which we demonstrate to be challenging for a general-purpose LLM. The second explicitly specifies the source of treatment variation in the prompt given to the LLM (e.g., by informing the LLM that the store is running an experiment). While this approach only allows the estimation of a conditional average treatment effect that depends on the specific experimental design, it provides valuable directional results for exploratory analysis.

Related papers

• Using LLMs for Explaining Sets of Counterfactual Examples to Final Users [0.0]
  In automated decision-making scenarios, causal inference methods can analyze the underlying data-generation process. Counterfactual examples explore hypothetical scenarios where a minimal number of factors are altered. We propose a novel multi-step pipeline that uses counterfactuals to generate natural language explanations of actions that will lead to a change in outcome.
  arXiv  Detail & Related papers  (2024-08-27T15:13:06Z)
• Simulating Field Experiments with Large Language Models [0.6144680854063939]
  This paper pioneers the utilization of large language models (LLMs) for simulating field experiments. By introducing two novel prompting strategies, observer and participant modes, we demonstrate the ability of LLMs to both predict outcomes and replicate participant responses within complex field settings. Our findings indicate a promising alignment with actual experimental results in certain scenarios, achieving a stimulation accuracy of 66% in observer mode.
  arXiv  Detail & Related papers  (2024-08-19T03:41:43Z)
• Do Large Language Models Exhibit Cognitive Dissonance? Studying the Difference Between Revealed Beliefs and Stated Answers [13.644277507363036]
  We investigate whether these abilities are measurable outside of tailored prompting and MCQ. Our findings suggest that the Revealed Belief of LLMs significantly differs from their Stated Answer. As text completion is at the core of LLMs, these results suggest that common evaluation methods may only provide a partial picture.
  arXiv  Detail & Related papers  (2024-06-21T08:56:35Z)
• Evaluating Interventional Reasoning Capabilities of Large Language Models [58.52919374786108]
  Large language models (LLMs) can estimate causal effects under interventions on different parts of a system. We conduct empirical analyses to evaluate whether LLMs can accurately update their knowledge of a data-generating process in response to an intervention. We create benchmarks that span diverse causal graphs (e.g., confounding, mediation) and variable types, and enable a study of intervention-based reasoning.
  arXiv  Detail & Related papers  (2024-04-08T14:15:56Z)
• Explaining Large Language Models Decisions Using Shapley Values [1.223779595809275]
  Large language models (LLMs) have opened up exciting possibilities for simulating human behavior and cognitive processes. However, the validity of utilizing LLMs as stand-ins for human subjects remains uncertain. This paper presents a novel approach based on Shapley values to interpret LLM behavior and quantify the relative contribution of each prompt component to the model's output.
  arXiv  Detail & Related papers  (2024-03-29T22:49:43Z)
• Comprehensive Reassessment of Large-Scale Evaluation Outcomes in LLMs: A Multifaceted Statistical Approach [64.42462708687921]
  Evaluations have revealed that factors such as scaling, training types, architectures and other factors profoundly impact the performance of LLMs. Our study embarks on a thorough re-examination of these LLMs, targeting the inadequacies in current evaluation methods. This includes the application of ANOVA, Tukey HSD tests, GAMM, and clustering technique.
  arXiv  Detail & Related papers  (2024-03-22T14:47:35Z)
• Are You Sure? Challenging LLMs Leads to Performance Drops in The FlipFlop Experiment [82.60594940370919]
  We propose the FlipFlop experiment to study the multi-turn behavior of Large Language Models (LLMs) We show that models flip their answers on average 46% of the time and that all models see a deterioration of accuracy between their first and final prediction, with an average drop of 17% (the FlipFlop effect) We conduct finetuning experiments on an open-source LLM and find that finetuning on synthetically created data can mitigate - reducing performance deterioration by 60% - but not resolve sycophantic behavior entirely.
  arXiv  Detail & Related papers  (2023-11-14T23:40:22Z)
• From Values to Opinions: Predicting Human Behaviors and Stances Using Value-Injected Large Language Models [10.520548925719565]
  We propose to use value-injected large language models (LLM) to predict opinions and behaviors. We conduct a series of experiments on four tasks to test the effectiveness of VIM. Results suggest that opinions and behaviors can be better predicted using value-injected LLMs than the baseline approaches.
  arXiv  Detail & Related papers  (2023-10-27T02:18:10Z)
• Mastering the Task of Open Information Extraction with Large Language Models and Consistent Reasoning Environment [52.592199835286394]
  Open Information Extraction (OIE) aims to extract objective structured knowledge from natural texts. Large language models (LLMs) have exhibited remarkable in-context learning capabilities.
  arXiv  Detail & Related papers  (2023-10-16T17:11:42Z)
• Counterfactual Maximum Likelihood Estimation for Training Deep Networks [83.44219640437657]
  Deep learning models are prone to learning spurious correlations that should not be learned as predictive clues. We propose a causality-based training framework to reduce the spurious correlations caused by observable confounders. We conduct experiments on two real-world tasks: Natural Language Inference (NLI) and Image Captioning.
  arXiv  Detail & Related papers  (2021-06-07T17:47:16Z)
• Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects [61.03579766573421]
  We study estimation of individual-level causal effects, such as a single patient's response to alternative medication. We devise representation learning algorithms that minimize our bound, by regularizing the representation's induced treatment group distance. We extend these algorithms to simultaneously learn a weighted representation to further reduce treatment group distances.
  arXiv  Detail & Related papers  (2020-01-21T10:16:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.