Summary of Online Personalizing White-box Llms Generation with Neural Bandits, by Zekai Chen et al.
Online Personalizing White-box LLMs Generation with Neural Bandits
by Zekai Chen, Weeden Daniel, Po-yu Chen, Francois Buet-Golfouse
First submitted to arxiv on: 24 Apr 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 presents an innovative online method for adapting text to individual preferences using neural bandit algorithms and soft instruction embeddings based on user feedback, enhancing personalized open-ended text generation by large language models (LLMs). The approach employs a novel neuralTS strategy that achieves significant performance improvements over baseline strategies, with substantial enhancements in personalized news headline generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study introduces an efficient way to adapt text to individual preferences without requiring unique models for each user. By using neural bandit algorithms and soft instruction embeddings based on user feedback, the approach enhances personalized open-ended text generation by LLMs. The results show significant performance improvements over baseline strategies, with substantial enhancements in personalized news headline generation. |
Keywords
» Artificial intelligence » Text generation
