Summary of Bright: a Realistic and Challenging Benchmark For Reasoning-intensive Retrieval, by Hongjin Su et al.
BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval
by Hongjin Su, Howard Yen, Mengzhou Xia, Weijia Shi, Niklas Muennighoff, Han-yu Wang, Haisu Liu, Quan Shi, Zachary S. Siegel, Michael Tang, Ruoxi Sun, Jinsung Yoon, Sercan O. Arik, Danqi Chen, Tao Yu
First submitted to arxiv on: 16 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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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 proposed paper introduces a novel text retrieval benchmark called BRIGHT, designed to evaluate retrieval models’ ability to handle complex real-world queries that require intensive reasoning. The benchmark consists of 1,384 diverse domain-specific queries drawn from human-generated data. Existing state-of-the-art retrieval models struggle on BRIGHT, with the top-performing model achieving an nDCG@10 score of only 18.3. The paper demonstrates that incorporating explicit reasoning about the query improves performance by up to 12.2 points and incorporating retrieved documents boosts question-answering performance by over 6.6 points. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary BRIGHT is a new way to test how well computers can find answers when you ask them questions. Right now, most computer systems are good at answering simple questions like what’s the weather like? But they struggle with more complicated questions that need deep thinking and understanding. The paper shows that even the best computer systems today don’t do very well on these tough questions. It also shows that if we help computers think about the question better, or use the answers they find to answer other related questions, it can make a big difference in how well they do. |
Keywords
» Artificial intelligence » Question answering
