Summary of Rafe: Ranking Feedback Improves Query Rewriting For Rag, by Shengyu Mao et al.
RaFe: Ranking Feedback Improves Query Rewriting for RAG
by Shengyu Mao, Yong Jiang, Boli Chen, Xiao Li, Peng Wang, Xinyu Wang, Pengjun Xie, Fei Huang, Huajun Chen, Ningyu Zhang
First submitted to arxiv on: 23 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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 proposed framework, ours, aims to train query rewriting models without requiring annotations or predesigned rewards for feedback. By leveraging a publicly available reranker, ours provides feedback aligned with the rewriting objectives. This approach has been shown to outperform baseline methods in experimental results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way of training language models is being developed! Researchers are working on creating models that can rewrite queries without needing extra information or special rewards. They’re using a helpful tool called a reranker to give their model the right feedback, and it seems to be working well. This could lead to better performance in tasks like answering questions. |
