Summary of Internalized Self-correction For Large Language Models, by Nishanth Upadhyaya and Raghavendra Sridharamurthy
Internalized Self-Correction for Large Language Models
by Nishanth Upadhyaya, Raghavendra Sridharamurthy
First submitted to arxiv on: 21 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this article, we introduce ‘Internalized Self-Correction’ (InSeC) for large language models (LLMs), which proposes a novel method combining negative sampling, self-reflection during training, and inference time. This approach allows LLMs to correct themselves by introducing mistakes and corrections during training, converting the learning process into a supervised task with positive and negative examples. InSeC can improve instruction following and correct hallucinations or incorrect sentences generated by LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces ‘Internalized Self-Correction’ (InSeC) for large language models (LLMs). It’s like teaching a big AI model to say “oops, I made a mistake!” during training. This helps the model learn from its mistakes and correct them when it makes errors. The goal is to improve how well the model follows instructions and generates accurate text. |
Keywords
» Artificial intelligence » Inference » Supervised
