Summary of A Systematic Examination Of Preference Learning Through the Lens Of Instruction-following, by Joongwon Kim et al.
A Systematic Examination of Preference Learning through the Lens of Instruction-Following
by Joongwon Kim, Anirudh Goyal, Aston Zhang, Bo Xiong, Rui Hou, Melanie Kambadur, Dhruv Mahajan, Hannaneh Hajishirzi, Liang Tan
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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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 investigates the impact of specific attributes in preference datasets on aligning large language models (LLMs) to human preferences and improving performance on instruction-following tasks. It uses a novel synthetic data generation pipeline to create 48,000 unique prompts with verifiable constraints, allowing for quality assessments of model responses. The authors experiment with two preference dataset curation methods, rejection sampling (RS) and Monte Carlo Tree Search (MCTS), and analyze the effects of shared prefixes between chosen and rejected responses, contrast and quality of responses, and complexity of training prompts. Results show that shared prefixes from MCTS provide minor but consistent improvements, high-contrast pairs generally outperform low-contrast pairs, and moderate-difficulty prompts lead to better generalization across tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how certain things in preference datasets affect large language models (LLMs) learning to follow instructions. It makes new data using a special method that lets it test the quality of the LLMs’ answers. The authors try two ways to curate these preferences and see what happens when they use different types of prompts or responses. They find out that some things make the LLMs learn better, like having similar parts in chosen and rejected answers or using high-quality prompts. |
Keywords
» Artificial intelligence » Generalization » Synthetic data
