Human Choice Prediction in Language-based Persuasion Games: Simulation-based Off-Policy Evaluation

• URL: http://arxiv.org/abs/2305.10361v4
• Date: Wed, 28 Feb 2024 21:36:54 GMT
• Title: Human Choice Prediction in Language-based Persuasion Games: Simulation-based Off-Policy Evaluation
• Authors: Eilam Shapira, Reut Apel, Moshe Tennenholtz, Roi Reichart
• Abstract summary: This paper addresses a key aspect in the design of such agents: Predicting human decision in off-policy evaluation. We collected a dataset of 87K decisions from humans playing a repeated decision-making game with artificial agents. Our approach involves training a model on human interactions with one agents subset to predict decisions when interacting with another.
• Score: 24.05034588588407
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in Large Language Models (LLMs) have spurred interest in designing LLM-based agents for tasks that involve interaction with human and artificial agents. This paper addresses a key aspect in the design of such agents: Predicting human decision in off-policy evaluation (OPE), focusing on language-based persuasion games, where the agent's goal is to influence its partner's decisions through verbal messages. Using a dedicated application, we collected a dataset of 87K decisions from humans playing a repeated decision-making game with artificial agents. Our approach involves training a model on human interactions with one agents subset to predict decisions when interacting with another. To enhance off-policy performance, we propose a simulation technique involving interactions across the entire agent space and simulated decision makers. Our learning strategy yields significant OPE gains, e.g., improving prediction accuracy in the top 15% challenging cases by 7.1%. Our code and the large dataset we collected and generated are submitted as supplementary material and publicly available in our GitHub repository: https://github.com/eilamshapira/HumanChoicePrediction

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