Summary of Judgerank: Leveraging Large Language Models For Reasoning-intensive Reranking, by Tong Niu et al.
JudgeRank: Leveraging Large Language Models for Reasoning-Intensive Reranking
by Tong Niu, Shafiq Joty, Ye Liu, Caiming Xiong, Yingbo Zhou, Semih Yavuz
First submitted to arxiv on: 31 Oct 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 paper introduces JudgeRank, a novel agentic reranker designed to address the limitations of large language models (LLMs) in reasoning-intensive retrieval-augmented generation (RAG) tasks. JudgeRank emulates human cognitive processes to assess document relevance, comprising query analysis, document summary extraction, and relevance judgment. The approach demonstrates substantial performance improvements over first-stage retrieval methods on the BRIGHT benchmark and performs on par with fine-tuned state-of-the-art rerankers on the BEIR benchmark. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary JudgeRank is a new way for computers to decide which documents are most relevant to a question. It’s better than current methods because it can think more like humans do when judging relevance. The system breaks down into three steps: understanding what the user wants, summarizing important points from each document, and making a final decision about how relevant each document is. JudgeRank was tested on some really hard questions and performed very well. |
Keywords
» Artificial intelligence » Rag » Retrieval augmented generation
