BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval
<|reference_start|>BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval: Existing retrieval benchmarks primarily consist of information-seeking queries (e.g., aggregated questions from search engines) where keyword or semantic-based retrieval is usually sufficient. However, many complex real-world queries require in-depth reasoning to identify relevant documents that go beyond surface form matching. For example, finding documentation for a coding question requires understanding the logic and syntax of the functions involved. To better benchmark retrieval on such challenging queries, we introduce BRIGHT, the first text retrieval benchmark that requires intensive reasoning to retrieve relevant documents. BRIGHT is constructed from the 1,398 real-world queries collected from diverse domains (such as economics, psychology, robotics, software engineering, earth sciences, etc.), sourced from naturally occurring or carefully curated human data. Extensive evaluation reveals that even state-of-the-art retrieval models perform poorly on BRIGHT. The leading model on the MTEB leaderboard [38 ], which achieves a score of 59.0 nDCG@10,2 produces a score of nDCG@10 of 18.0 on BRIGHT. We further demonstrate that augmenting queries with Chain-of-Thought reasoning generated by large language models (LLMs) improves performance by up to 12.2 points. Moreover, BRIGHT is robust against data leakage during pretraining of the benchmarked models as we validate by showing similar performance even when documents from the benchmark are included in the training data. We believe that BRIGHT paves the way for future research on retrieval systems in more realistic and challenging settings. Our code and data are available at https://brightbenchmark.github.io.<|reference_end|>
@article{su2024bright:,
    title={BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive
  Retrieval},
    author={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},
    journal={arXiv preprint arXiv:2407.12883},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.12883},
    primaryClass={cs.CL cs.AI cs.IR}
}