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

arXiv — cs.CL•Thursday, November 13, 2025 at 5:00:00 AM

The study on self-correcting large language models, published on arXiv, systematically investigates how these models refine their responses through self-correction mechanisms. It compares performance trends and error-correction behaviors in two distinct contexts: open-ended text generation and multiple-choice selection. The results reveal that open-ended generation allows for greater flexibility and compositional refinement, leading to improved outcomes, while multiple-choice selection is limited by the predefined options available. This contrast underscores the dual demands faced by emerging agentic applications of LLMs, emphasizing the need for effective design that considers the interaction between task structure and output space. Understanding these dynamics is essential for advancing AI technologies and enhancing their performance across various natural language understanding and reasoning tasks.

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