Pride and Prejudice: LLM Amplifies Self-Bias in Self-Refinement
arxiv(2024)
摘要
Recent studies show that large language models (LLMs) improve their
performance through self-feedback on certain tasks while degrade on others. We
discovered that such a contrary is due to LLM's bias in evaluating their own
output. In this paper, we formally define LLM's self-bias - the tendency to
favor its own generation - using two statistics. We analyze six LLMs (GPT-4,
GPT-3.5, Gemini, LLaMA2, Mixtral and DeepSeek) on translation, constrained text
generation, and mathematical reasoning tasks. We find that self-bias is
prevalent in all examined LLMs across multiple languages and tasks. Our
analysis reveals that while the self-refine pipeline improves the fluency and
understandability of model outputs, it further amplifies self-bias. To mitigate
such biases, we discover that larger model size and external feedback with
accurate assessment can significantly reduce bias in the self-refine pipeline,
leading to actual performance improvement in downstream tasks. The code and
data are released at https://github.com/xu1998hz/llm_self_bias.
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