Summary of Iopo: Empowering Llms with Complex Instruction Following Via Input-output Preference Optimization, by Xinghua Zhang et al.
IOPO: Empowering LLMs with Complex Instruction Following via Input-Output Preference Optimization
by Xinghua Zhang, Haiyang Yu, Cheng Fu, Fei Huang, Yongbin Li
First submitted to arxiv on: 9 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 introduces a new benchmark called TRACE for evaluating the ability of large language models (LLMs) to follow complex instructions, as well as an alignment method called IOPO that improves this ability. The authors demonstrate the effectiveness of IOPO on both in-domain and out-of-domain datasets, achieving improvements over existing methods SFT and DPO. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way for large language models (LLMs) to understand instructions better. It makes a special test called TRACE that helps measure how well LLMs can follow complex instructions. The authors also came up with an idea called IOPO that helps LLMs learn what people like and dislike, so they can give more helpful answers. They tested IOPO on some datasets and showed that it works better than other methods. |
Keywords
» Artificial intelligence » Alignment
