Summary of Rankrag: Unifying Context Ranking with Retrieval-augmented Generation in Llms, by Yue Yu et al.
RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMs
by Yue Yu, Wei Ping, Zihan Liu, Boxin Wang, Jiaxuan You, Chao Zhang, Mohammad Shoeybi, Bryan Catanzaro
First submitted to arxiv on: 2 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); 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 A novel instruction fine-tuning framework called RankRAG is proposed for retrieval-augmented generation (RAG), which instruction-tunes a single large language model (LLM) for context ranking and answer generation. This approach outperforms existing expert ranking models, including LLMs exclusively fine-tuned on ranking data. For generation, the proposed model, Llama3-RankRAG, significantly outperforms strong baselines like GPT-4-0613, GPT-4-turbo-2024-0409, and ChatQA-1.5 on nine knowledge-intensive benchmarks. It also demonstrates generalization capability to new domains, comparable to GPT-4, without instruction fine-tuning on biomedical data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RankRAG is a new way to use big language models. Instead of just training them for generation, it teaches them to rank and generate answers at the same time. This works really well, even with just a small amount of extra data. It’s better than other ways of doing this, like using expert ranking models or training on lots of data. The new model, Llama3-RankRAG, is especially good and beats other strong models on many tasks. |
Keywords
* Artificial intelligence * Fine tuning * Generalization * Gpt * Large language model * Rag * Retrieval augmented generation
