Self-Correcting Large Language Models: Generation vs. Multiple Choice

TL;DR

This paper systematically compares self-correction in large language models across open-ended generation and multiple-choice tasks, revealing distinct behaviors and implications for designing effective correction mechanisms.

Contribution

It provides a comprehensive analysis of how self-correction varies between generation and multiple-choice paradigms in LLMs, highlighting the importance of task structure.

Findings

Generation benefits from reinterpretation and refinement.

Multiple-choice leverages clear solution boundaries.

Different failure modes observed in each paradigm.

Abstract

Large language models have recently demonstrated remarkable abilities to self-correct their responses through iterative refinement, often referred to as self-consistency or self-reflection. However, the dynamics of this self-correction mechanism may differ substantially depending on whether the model is tasked with open-ended text generation or with selecting the most appropriate response from multiple predefined options. In this paper, we conduct a systematic investigation of these two paradigms by comparing performance trends and error-correction behaviors across various natural language understanding and reasoning tasks, covering language models of different scales and families. Our experimental results reveal distinct patterns of improvement and failure modes: \textit{While open-ended generation often benefits from the flexibility of re-interpretation and compositional refinement, multiple-choice selection can leverage clearer solution boundaries but may be limited by the provided options}. This contrast also reflects the dual demands faced by emerging agentic LLM applications: effective agents must not only generate and refine open-ended plans or explanations, but also make reliable discrete choices when operating within constrained action spaces. Our findings, therefore, highlight that the design of self-correction mechanisms should take into account the interaction between task structure and output space, with implications for both knowledge-intensive reasoning and decision-oriented applications of LLMs.