Summary of Spar: Self-play with Tree-search Refinement to Improve Instruction-following in Large Language Models, by Jiale Cheng et al.
SPaR: Self-Play with Tree-Search Refinement to Improve Instruction-Following in Large Language Models
by Jiale Cheng, Xiao Liu, Cunxiang Wang, Xiaotao Gu, Yida Lu, Dan Zhang, Yuxiao Dong, Jie Tang, Hongning Wang, Minlie Huang
First submitted to arxiv on: 16 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 research paper proposes a novel approach to instruction-following in language models, known as Self-Play framework (SPaR), which enables models to recognize subtle requirements in instructions and accurately reflect them in their output. By integrating tree-search self-refinement, SPaR generates valid and comparable preference pairs that minimize distractions, allowing the model to focus on recognizing key differences that lead to improved instruction following. The authors demonstrate the effectiveness of SPaR by training an LLaMA3-8B model over three iterations, which surpasses GPT-4-Turbo on the IFEval benchmark while maintaining general capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about teaching language models to follow instructions exactly. Right now, these models are good at making up their own responses, but they don’t always get the instruction right. The researchers created a new way to train models called Self-Play framework (SPaR) that helps them understand what’s important in an instruction and make better responses. They tested SPaR on several language models and found that it works well and even makes some models better than others at following instructions. |
Keywords
» Artificial intelligence » Gpt
