Summary of Reformatted Alignment, by Run-ze Fan et al.
Reformatted Alignment
by Run-Ze Fan, Xuefeng Li, Haoyang Zou, Junlong Li, Shwai He, Ethan Chern, Jiewen Hu, Pengfei Liu
First submitted to arxiv on: 19 Feb 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 The paper introduces ReAlign, a simple yet effective approach to improve the quality of instruction data used for finetuning large language models (LLMs). By reformating responses to better align with established criteria and collated evidence, ReAlign minimizes human annotation, hallucination, and scaling difficulties. This technique orthogonal to existing alignment methods improves LLMs’ general alignment ability, math reasoning, factuality, and readability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps large language models better match human values by improving the quality of instruction data. The team develops a new approach called ReAlign that makes instruction data more accurate and aligning with what humans want. This method is easy to use, doesn’t require a lot of human effort, and works well even when dealing with large amounts of data. |
Keywords
* Artificial intelligence * Alignment * Hallucination
