The emergence of large language models (LLMs) has opened up exciting possibilities for simulating human behavior and cognitive processes, with potential applications in various domains, including marketing research and consumer behavior analysis. However, the validity of utilizing LLMs as stand-ins for human subjects remains uncertain due to glaring divergences that suggest fundamentally different underlying processes at play and the sensitivity of LLM responses to prompt variations. This paper presents a novel approach based on Shapley values from cooperative game theory to interpret LLM behavior and quantify the relative contribution of each prompt component to the model's output. Through two applications - a discrete choice experiment and an investigation of cognitive biases - we demonstrate how the Shapley value method can uncover what we term "token noise" effects, a phenomenon where LLM decisions are disproportionately influenced by tokens providing minimal informative content. This phenomenon raises concerns about the robustness and generalizability of insights obtained from LLMs in the context of human behavior simulation. Our model-agnostic approach extends its utility to proprietary LLMs, providing a valuable tool for practitioners and researchers to strategically optimize prompts and mitigate apparent cognitive biases. Our findings underscore the need for a more nuanced understanding of the factors driving LLM responses before relying on them as substitutes for human subjects in survey settings. We emphasize the importance of researchers reporting results conditioned on specific prompt templates and exercising caution when drawing parallels between human behavior and LLMs.

翻译：大语言模型（LLMs）的出现为模拟人类行为与认知过程开辟了新的可能性，在市场营销研究和消费者行为分析等多个领域展现出应用潜力。然而，由于LLMs与人类存在显著差异——这暗示其底层运作机制存在本质区别，且LLM的响应对提示词变动极为敏感——将其作为人类受试者替代品的有效性仍存疑。本文提出一种基于合作博弈论中Shapley值的新方法，用以解释LLM行为并量化各提示词成分对模型输出的相对贡献。通过离散选择实验和认知偏差研究两项应用，我们展示了Shapley值方法如何揭示所谓的"令牌噪声"效应：即LLM的决策过程被信息含量极低的令牌过度影响的现象。该现象引发了对LLMs在人类行为模拟场景中所获结论的鲁棒性与可推广性的担忧。我们提出的模型无关方法可扩展至专有LLMs，为从业者和研究者提供优化提示词策略、缓解显性认知偏差的重要工具。研究结果强调，在将LLMs作为调查环境中人类受试者替代品之前，需要更细致地理解驱动LLM响应的内在因素。我们特别指出，研究者应报告基于特定提示模板的实验结果，并在建立人类行为与LLMs之间的关联时保持审慎态度。