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

• URL: http://arxiv.org/abs/2312.15524v2
• Date: Tue, 21 Jan 2025 21:34:52 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 shown impressive potential to simulate human behavior.<n>We identify a fundamental challenge in using them to simulate experiments.<n>When LLM-simulated subjects are blind to the experimental design, variations in treatment systematically affect unspecified variables.
• Score: 0.27624021966289597
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have shown impressive potential to simulate human behavior. We identify a fundamental challenge in using them to simulate experiments: when LLM-simulated subjects are blind to the experimental design (as is standard practice with human subjects), variations in treatment systematically affect unspecified variables that should remain constant, violating the unconfoundedness assumption. Using demand estimation as a context and an actual experiment as a benchmark, we show this can lead to implausible results. While confounding may in principle be addressed by controlling for covariates, this can compromise ecological validity in the context of LLM simulations: controlled covariates become artificially salient in the simulated decision process, which introduces focalism. This trade-off between unconfoundedness and ecological validity is usually absent in traditional experimental design and represents a unique challenge in LLM simulations. We formalize this challenge theoretically, showing it stems from ambiguous prompting strategies, and hence cannot be fully addressed by improving training data or by fine-tuning. Alternative approaches that unblind the experimental design to the LLM show promise. Our findings suggest that effectively leveraging LLMs for experimental simulations requires fundamentally rethinking established experimental design practices rather than simply adapting protocols developed for human subjects.

Related papers

• Model-free Methods for Event History Analysis and Efficient Adjustment (PhD Thesis) [55.2480439325792]
  This thesis is a series of independent contributions to statistics unified by a model-free perspective. The first chapter elaborates on how a model-free perspective can be used to formulate flexible methods that leverage prediction techniques from machine learning. The second chapter studies the concept of local independence, which describes whether the evolution of one process is directly influenced by another.
  arXiv  Detail & Related papers  (2025-02-11T19:24:09Z)
• Causal Lifting of Neural Representations: Zero-Shot Generalization for Causal Inferences [56.23412698865433]
  We focus on causal inferences on a target experiment with unlabeled factual outcomes, retrieved by a predictive model fine-tuned on a labeled similar experiment. First, we show that factual outcome estimation via Empirical Risk Minimization (ERM) may fail to yield valid causal inferences on the target population. We propose Deconfounded Empirical Risk Minimization (DERM), a new simple learning procedure minimizing the risk over a fictitious target population.
  arXiv  Detail & Related papers  (2025-02-10T10:52:17Z)
• Can LLMs Reliably Simulate Human Learner Actions? A Simulation Authoring Framework for Open-Ended Learning Environments [1.4999444543328293]
  Simulating learner actions helps stress-test open-ended interactive learning environments and prototype new adaptations before deployment. We propose Hyp-Mix, a simulation authoring framework that allows experts to develop and evaluate simulations by combining testable hypotheses about learner behavior.
  arXiv  Detail & Related papers  (2024-10-03T00:25:40Z)
• 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)
• A Theory of LLM Sampling: Part Descriptive and Part Prescriptive [53.08398658452411]
  Large Language Models (LLMs) are increasingly utilized in autonomous decision-making. We show that this sampling behavior resembles that of human decision-making. We show that this deviation of a sample from the statistical norm towards a prescriptive component consistently appears in concepts across diverse real-world domains.
  arXiv  Detail & Related papers  (2024-02-16T18:28:43Z)
• LLM-driven Imitation of Subrational Behavior : Illusion or Reality? [3.2365468114603937]
  Existing work highlights the ability of Large Language Models to address complex reasoning tasks and mimic human communication. We propose to investigate the use of LLMs to generate synthetic human demonstrations, which are then used to learn subrational agent policies. We experimentally evaluate the ability of our framework to model sub-rationality through four simple scenarios.
  arXiv  Detail & Related papers  (2024-02-13T19:46:39Z)
• 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)
• Online simulator-based experimental design for cognitive model selection [74.76661199843284]
  We propose BOSMOS: an approach to experimental design that can select between computational models without tractable likelihoods. In simulated experiments, we demonstrate that the proposed BOSMOS technique can accurately select models in up to 2 orders of magnitude less time than existing LFI alternatives.
  arXiv  Detail & Related papers  (2023-03-03T21:41:01Z)
• Sequential Causal Imitation Learning with Unobserved Confounders [82.22545916247269]
  "Monkey see monkey do" is an age-old adage, referring to na"ive imitation without a deep understanding of a system's underlying mechanics. This paper investigates the problem of causal imitation learning in sequential settings, where the imitator must make multiple decisions per episode.
  arXiv  Detail & Related papers  (2022-08-12T13:53:23Z)
• On the Importance of Application-Grounded Experimental Design for Evaluating Explainable ML Methods [20.2027063607352]
  We present an experimental study extending a prior explainable ML evaluation experiment and bringing the setup closer to the deployment setting. Our empirical study draws dramatically different conclusions than the prior work, highlighting how seemingly trivial experimental design choices can yield misleading results. We believe this work holds lessons about the necessity of situating the evaluation of any ML method and choosing appropriate tasks, data, users, and metrics to match the intended deployment contexts.
  arXiv  Detail & Related papers  (2022-06-24T14:46:19Z)
• Likelihood-Free Inference in State-Space Models with Unknown Dynamics [71.94716503075645]
  We introduce a method for inferring and predicting latent states in state-space models where observations can only be simulated, and transition dynamics are unknown. We propose a way of doing likelihood-free inference (LFI) of states and state prediction with a limited number of simulations.
  arXiv  Detail & Related papers  (2021-11-02T12:33:42Z)
• Simulation as Experiment: An Empirical Critique of Simulation Research on Recommender Systems [4.006331916849688]
  We argue that simulation studies of recommender system (RS) evolution are conceptually similar to empirical experimental approaches. By adopting standards and practices common in empirical disciplines, simulation researchers can mitigate many of these weaknesses.
  arXiv  Detail & Related papers  (2021-07-29T21:05:01Z)
• 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.