Summary of Large Language Models Can Self-improve in Long-context Reasoning, by Siheng Li et al.
Large Language Models Can Self-Improve in Long-context Reasoning
by Siheng Li, Cheng Yang, Zesen Cheng, Lemao Liu, Mo Yu, Yujiu Yang, Wai Lam
First submitted to arxiv on: 12 Nov 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 paper explores the potential for Large Language Models (LLMs) to improve their performance in processing long contexts through self-improvement. Existing approaches rely on human annotations or advanced models like GPT-4, limiting progress. The authors propose a new approach called , which fine-tunes LLMs using Minimum Bayes Risk and preference optimization based on sampled outputs. Experiments demonstrate the effectiveness of , with an absolute improvement of 4.2 points for Llama-3.1-8B-Instruct. Compared to prior approaches relying on human-expert or advanced-model-generated data, achieves superior performance. This work is expected to open new avenues for self-improvement techniques in long-context scenarios, essential for the advancement of LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how big language models can get better at understanding long texts on their own. Right now, these models need help from humans or more advanced models like GPT-4 to improve. The authors came up with a new way called that lets the model learn from its own mistakes and correct them. They tested it on several language models and found that it worked really well, even better than previous approaches that relied on human help. This breakthrough could lead to new ways for big language models to keep getting smarter. |
Keywords
» Artificial intelligence » Gpt » Llama » Optimization
