Exploring the Sensitivity of LLMs' Decision-Making Capabilities: Insights from Prompt Variations and Hyperparameters
Submission Type: Regular Short Paper
Submission Track: Language Modeling and Analysis of Language Models
Keywords: Language Models, Decision-Making, Cognitive Psychology, Chain of Thought
TL;DR: This study shows that Large Language Models' decision-making abilities vary based on prompts and hyperparameters, contrary to previous findings.
Abstract: The advancement of Large Language Models (LLMs) has led to their widespread use across a broad spectrum of tasks, including decision-making. Prior studies have compared the decision-making abilities of LLMs with those of humans from a psychological perspective. However, these studies have not always properly accounted for the sensitivity of LLMs’ behavior to hyperparameters and variations in the prompt. In this study, we examine LLMs’ performance on the Horizon decision-making task studied by Binz and Schulz (2023), analyzing how LLMs respond to variations in prompts and hyperparameters. By experimenting on three OpenAI language models possessing different capabilities, we observe that the decision-making abilities fluctuate based on the input prompts and temperature settings. Contrary to previous findings, language models display a human-like exploration–exploitation tradeoff after simple adjustments to the prompt.
Submission Number: 4633
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