Six Fallacies in Substituting Large Language Models for Human Participants
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Key Points
Identifying six interpretive fallacies reveals major misconceptions about large language models and human cognition, indicating that they cannot fully replace human participants.
The analysis highlights both technical and conceptual limitations of LLMs, emphasizing the crucial differences between machine outputs and biological processes.
Recommendations for responsible research practices are provided to guide the use of AI systems as simulation tools rather than replacements for human participants.
This framework supports using LLMs for role-play and hypothesis testing while acknowledging their limitations in understanding human thought.
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Implication
Critical analysis uncovers six fallacies in considering AI systems as substitutes for human participants in research, indicating their limitations.