Summary of Progressively Label Enhancement For Large Language Model Alignment, by Biao Liu et al.
Progressively Label Enhancement for Large Language Model Alignment
by Biao Liu, Ning Xu, Xin Geng
First submitted to arxiv on: 5 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 The proposed framework, Progressively Label Enhancement (PLE), aims to address challenges in Large Language Models (LLM) alignment by dynamically adjusting the model’s training process based on the evolving quality of generated data. By prompting the model to generate responses for both original queries and principle-guided queries, PLE utilizes a dynamic threshold to determine the appropriate training approach. This framework outperforms existing LLM alignment methods in experimental results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) need to be aligned with human expectations to prevent misaligned content that can cause ethical and legal concerns. Researchers are exploring alternative methods to achieve this, but they often rely on large high-quality datasets. The proposed framework, PLE, solves the problem by dynamically adjusting the model’s training process based on the generated data quality. It generates responses for original queries and principle-guided queries, then determines the best training approach using a dynamic threshold. This method works better than existing LLM alignment methods. |
Keywords
» Artificial intelligence » Alignment » Prompting
