Summary of Speculative Rag: Enhancing Retrieval Augmented Generation Through Drafting, by Zilong Wang et al.
Speculative RAG: Enhancing Retrieval Augmented Generation through Drafting
by Zilong Wang, Zifeng Wang, Long Le, Huaixiu Steven Zheng, Swaroop Mishra, Vincent Perot, Yuwei Zhang, Anush Mattapalli, Ankur Taly, Jingbo Shang, Chen-Yu Lee, Tomas Pfister
First submitted to arxiv on: 11 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 abstract introduces Speculative Retrieval Augmented Generation (RAG), a framework that combines large language models (LLMs) with external knowledge sources to generate accurate and up-to-date responses. The paper presents an iterative refinement approach, leveraging larger generalist LLMs to verify multiple RAG drafts produced in parallel by smaller specialist LLMs. This approach reduces input token counts per draft, enhances comprehension of each subset, and mitigates position bias over long context. The method accelerates RAG by delegating drafting to the smaller specialist LM, with the larger generalist LM performing a single verification pass. The paper reports state-of-the-art performance on TriviaQA, MuSiQue, PopQA, PubHealth, and ARC-Challenge benchmarks, achieving up to 12.97% accuracy improvement and reducing latency by 50.83% compared to conventional RAG systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Speculative Retrieval Augmented Generation (RAG) is a new way to make computers generate answers that are more accurate and up-to-date. This is done by combining large language models with information from the internet. The researchers have created a system that uses two types of language models: one that generates lots of possible answers, and another that checks if those answers are correct. This makes the process faster and more accurate. The system was tested on several different tasks and performed better than other systems in some cases. |
Keywords
» Artificial intelligence » Rag » Retrieval augmented generation » Token
