Perils of Self-Feedback: Self-Bias Amplifies in Large Language Models
Wenda Xu Guanglei Zhu Xuandong Zhao Liangming Pan Lei Li William Yang Wang
Recent studies show that self-feedback improves large language models (LLMs)
on certain tasks while worsens other tasks. We discovered that such a contrary
is due to LLM's bias towards 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 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.