Self-Consistency Falls Short! The Adverse Effects of Positional Bias on Long-Context Problems

Abstract: Self-consistency (SC) has been demonstrated to enhance the performance of LLMs across various tasks and domains involving short content. However, does this evidence support its effectiveness for long-context problems? We challenge the assumption that SC's benefits generalize to long-context settings, where LLMs often struggle with position bias--a systematic tendency to over-rely on specific context regions-which hinders their ability to utilize information effectively from all parts of their context. Through comprehensive experimentation with varying state-of-the-art models and tasks, we find that SC not only fails to improve but actively degrades performance on long-context tasks. This degradation appears driven by persistent position bias, worsening with longer context lengths and smaller model sizes, but invariant to prompt format or task type. Unlike short-context tasks, where SC diversifies reasoning paths, long-context SC amplifies positional errors. These comprehensive results provide valuable insight into the limitations of current LLMs in long-context understanding and highlight the need for more sophisticated approaches.