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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.16653 |
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| _version_ | 1866929644282839040 |
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| author | Upadhyaya, Nishanth Sridharamurthy, Raghavendra |
| author_facet | Upadhyaya, Nishanth Sridharamurthy, Raghavendra |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_16653 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Internalized Self-Correction for Large Language Models Upadhyaya, Nishanth Sridharamurthy, Raghavendra Artificial Intelligence 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. |
| title | Internalized Self-Correction for Large Language Models |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2412.16653 |