Summary of Self-exploring Language Models: Active Preference Elicitation For Online Alignment, by Shenao Zhang et al.
Self-Exploring Language Models: Active Preference Elicitation for Online Alignment
by Shenao Zhang, Donghan Yu, Hiteshi Sharma, Han Zhong, Zhihan Liu, Ziyi Yang, Shuohang Wang, Hany Hassan, Zhaoran Wang
First submitted to arxiv on: 29 May 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 proposed Self-Exploring Language Models (SELM) algorithm optimizes preference alignment for Large Language Models (LLMs) by iteratively updating the model with a bilevel objective. This approach eliminates the need for a separate reward model and reduces indiscriminate favor of unseen extrapolations. By solving the inner-level problem with a reparameterized reward function, SELM enhances exploration efficiency and boosts performance on instruction-following benchmarks like MT-Bench and AlpacaEval 2.0, as well as various standard academic benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers understand what humans want them to do by creating better alignment between Large Language Models (LLMs) and human intentions. The researchers created a new algorithm called SELM that makes LLMs better at following instructions. This is important because it can help us use these powerful language models for more tasks, like generating text or translating languages. |
Keywords
» Artificial intelligence » Alignment
