Summary of Knowpo: Knowledge-aware Preference Optimization For Controllable Knowledge Selection in Retrieval-augmented Language Models, by Ruizhe Zhang et al.
KnowPO: Knowledge-aware Preference Optimization for Controllable Knowledge Selection in Retrieval-Augmented Language Models
by Ruizhe Zhang, Yongxin Xu, Yuzhen Xiao, Runchuan Zhu, Xinke Jiang, Xu Chu, Junfeng Zhao, Yasha Wang
First submitted to arxiv on: 6 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposes a Knowledge-aware Preference Optimization (KnowPO) strategy to enhance the knowledge selection of large language models (LLMs) in various contexts. RAG, which integrates external knowledge, has been effective in mitigating hallucination problems in LLMs, but it can lead to knowledge conflicts. The authors refine instruction-tuning by introducing explicit negative signals and comparative objectives to avoid undesirable behaviors like contextual ignorance and overinclusion. KnowPO constructs a dataset covering various error types and learns to avoid these signals through preference optimization methods. Experimental results show that KnowPO outperforms previous methods for handling knowledge conflicts by 37% and exhibits robust generalization across datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) are super smart, but they can get confused when dealing with lots of information. To help them make better decisions, the authors developed a new strategy called KnowPO. It’s like teaching a model how to choose what it knows is most relevant to the situation. The old way of doing this had some problems, so the authors created a special dataset that covers all sorts of mistakes and learned how to avoid making those same mistakes in the future. When they tested their new strategy, they found that it worked much better than before! |
Keywords
» Artificial intelligence » Generalization » Hallucination » Instruction tuning » Optimization » Rag
