Summary of Human-instruction-free Llm Self-alignment with Limited Samples, by Hongyi Guo et al.
Human-Instruction-Free LLM Self-Alignment with Limited Samples
by Hongyi Guo, Yuanshun Yao, Wei Shen, Jiaheng Wei, Xiaoying Zhang, Zhaoran Wang, Yang Liu
First submitted to arxiv on: 6 Jan 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 This paper proposes an innovative approach to aligning large language models (LLMs) with human values, addressing the limitations of current methods. The algorithm can self-align LLMs iteratively without requiring active human involvement or annotated data. It uses In-context Learning examples to generate more samples and finetunes the LLM iteratively. This method shows promise in unlocking the LLM’s self-generalization ability, enabling alignment with near-zero human supervision. The authors test their algorithm on three benchmarks, demonstrating good performance in alignment, domain adaptability, and scalability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make language models align better with our values. Right now, making language models align is hard because it needs a lot of data and people to help. This new method can do it without needing so much help from humans. It works by finding good examples related to the topic and using those to teach the model more things. Then, it uses what it learned to make itself better at aligning. The researchers tested this method on three topics and showed that it does a great job of making language models align with our values. |
Keywords
* Artificial intelligence * Alignment * Generalization
