LLMs like GPT-4 accurately predict human decision-making biases

A new study published on arXiv explores the ability of large language models (LLMs) to predict human decision-making biases in conversational settings. Researchers found that LLMs, including GPT-4 and GPT-5, could accurately predict human biases like the Framing Effect and Status Quo Bias, especially when incorporating dialogue context. The study also revealed that increased cognitive load, simulated through complex dialogues, amplified these biases in humans, a pattern that the LLMs were also able to reproduce. Notably, GPT-4 models demonstrated superior performance compared to GPT-5 and other open-source models in accurately mirroring human behavior and bias patterns. AI

IMPACT LLMs can be used to simulate and understand human cognitive biases, potentially leading to more adaptive conversational agents.

RANK_REASON The cluster is based on an academic paper published on arXiv detailing research findings. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• alphaXiv
• arXiv
• Framing Effect
• GPT-4
• GPT-5
• Hugging Face
• Status Quo Bias
• Stephen Pilling

• paper
• safety

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