Internalized Self-Correction for Large Language Models

Nishanth Upadhyaya; Raghavendra Sridharamurthy

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arXiv · 2412.16653

Internalized Self-Correction for Large Language Models

Nishanth Upadhyaya·Raghavendra Sridharamurthy

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Abstract

In this article, we introduce 'Internalized Self-Correction' (InSeC) for large language models (LLMs). While many approaches exist for self-reflection at inference time, we propose a novel method that combines ideas from negative sampling, self-reflection during training, and inference time. InSeC allows LLMs to correct themselves by introducing mistakes and their corresponding corrections during training, thereby converting the learning process into a true supervised learning task with both positive and negative examples. This approach can be extended to improve instruction following and correct hallucinations or incorrect sentences generated by LLMs.

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Nishanth Upadhyaya, Raghavendra Sridharamurthy. 2024-12-21. Internalized Self-Correction for Large Language Models. https://arxiv.org/abs/2412.16653

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