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

• URL: http://arxiv.org/abs/2511.09381v1
• Date: Thu, 13 Nov 2025 01:50:43 GMT
• Title: Self-Correcting Large Language Models: Generation vs. Multiple Choice
• Authors: Hossein A. Rahmani, Satyapriya Krishna, Xi Wang, Mohammadmehdi Naghiaei, Emine Yilmaz,
• Abstract summary: Large language models have recently demonstrated remarkable abilities to self-correct their responses through iterative refinement.<n>We compare performance trends and error-correction behaviors across various natural language understanding and reasoning tasks.<n>Our findings highlight that the design of self-correction mechanisms should take into account the interaction between task structure and output space.
• Score: 29.697851249014192
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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