Systematic Biases in LLM Simulations of Debates

Recent advancements in natural language processing, especially the emergence of Large Language Models (LLMs), have opened exciting possibilities for constructing computational simulations designed to replicate human behavior accurately. However, LLMs are complex statistical learners without straightforward deductive rules, making them prone to unexpected behaviors. In this study, we highlight the limitations of LLMs in simulating human interactions, particularly focusing on LLMs' ability to simulate political debates. Our findings indicate a tendency for LLM agents to conform to the model's inherent …

behavior biases computational cs.ai cs.cl emergence highlight human language language models language processing large language large language models limitations llm llms making natural natural language natural language processing processing replicate rules simulations statistical study them