Enhancing Self-Correction in Large Language Models through Multi-Perspective Reflection

• URL: http://arxiv.org/abs/2601.07780v1
• Date: Mon, 12 Jan 2026 17:57:05 GMT
• Title: Enhancing Self-Correction in Large Language Models through Multi-Perspective Reflection
• Authors: Mariana Costa, Alberlucia Rafael Soarez, Daniel Kim, Camila Ferreira,
• Abstract summary: MyGO Poly-Reflective Chain-of-Thought (PR-CoT) is a novel methodology employing structured multi-perspective reflection.<n>It refines the initial CoT into a more robust and accurate final answer without model retraining.<n>It significantly outperforms traditional CoT and existing reflection methods in logical consistency and error correction.
• Score: 0.33625320078410365
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While Chain-of-Thought (CoT) prompting advances LLM reasoning, challenges persist in consistency, accuracy, and self-correction, especially for complex or ethically sensitive tasks. Existing single-dimensional reflection methods offer insufficient improvements. We propose MyGO Poly-Reflective Chain-of-Thought (PR-CoT), a novel methodology employing structured multi-perspective reflection. After initial CoT, PR-CoT guides the LLM to self-assess its reasoning across multiple predefined angles: logical consistency, information completeness, biases/ethics, and alternative solutions. Implemented purely via prompt engineering, this process refines the initial CoT into a more robust and accurate final answer without model retraining. Experiments across arithmetic, commonsense, ethical decision-making, and logical puzzles, using GPT-three point five and GPT-four models, demonstrate PR-CoT's superior performance. It significantly outperforms traditional CoT and existing reflection methods in logical consistency and error correction, with notable gains in nuanced domains like ethical decision-making. Ablation studies, human evaluations, and qualitative analyses further validate the contribution of each reflection perspective and the overall efficacy of our poly-reflective paradigm in fostering more reliable LLM reasoning.

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