BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval

• URL: http://arxiv.org/abs/2407.12883v3
• Date: Thu, 24 Oct 2024 04:51:21 GMT
• Title: BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval
• Authors: 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,
• Abstract summary: Many complex real-world queries require in-depth reasoning to identify relevant documents. We introduce BRIGHT, the first text retrieval benchmark that requires intensive reasoning to retrieve relevant documents. Our dataset consists of 1,384 real-world queries spanning diverse domains, such as economics, psychology, mathematics, and coding.
• Score: 54.54576644403115
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. Our dataset consists of 1,384 real-world queries spanning diverse domains, such as economics, psychology, mathematics, and coding. These queries are drawn from naturally occurring and 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 (Muennighoff et al., 2023), which achieves a score of 59.0 nDCG@10, produces a score of nDCG@10 of 18.3 on BRIGHT. We show that incorporating explicit reasoning about the query improves retrieval performance by up to 12.2 points. Moreover, incorporating retrieved documents from the top-performing retriever boosts question-answering performance by over 6.6 points. We believe that BRIGHT paves the way for future research on retrieval systems in more realistic and challenging settings.

Related papers

• Disentangling Questions from Query Generation for Task-Adaptive Retrieval [22.86406485412172]
  We propose EGG, a query generator that better adapts to wide search intents expressed in the BeIR benchmark. Our method outperforms baselines and existing models on four tasks with underexplored intents, while utilizing a query generator 47 times smaller than the previous state-of-the-art.
  arXiv  Detail & Related papers  (2024-09-25T02:53:27Z)
• STaRK: Benchmarking LLM Retrieval on Textual and Relational Knowledge Bases [93.96463520716759]
  We develop STARK, a large-scale Semi-structure retrieval benchmark on Textual and Knowledge Bases. Our benchmark covers three domains: product search, academic paper search, and queries in precision medicine. We design a novel pipeline to synthesize realistic user queries that integrate diverse relational information and complex textual properties.
  arXiv  Detail & Related papers  (2024-04-19T22:54:54Z)
• Ask Optimal Questions: Aligning Large Language Models with Retriever's Preference in Conversational Search [25.16282868262589]
  RetPO is designed to optimize a language model (LM) for reformulating search queries in line with the preferences of the target retrieval systems. We construct a large-scale dataset called Retrievers' Feedback on over 410K query rewrites across 12K conversations. The resulting model achieves state-of-the-art performance on two recent conversational search benchmarks.
  arXiv  Detail & Related papers  (2024-02-19T04:41:31Z)
• Decomposing Complex Queries for Tip-of-the-tongue Retrieval [72.07449449115167]
  Complex queries describe content elements (e.g., book characters or events), information beyond the document text. This retrieval setting, called tip of the tongue (TOT), is especially challenging for models reliant on lexical and semantic overlap between query and document text. We introduce a simple yet effective framework for handling such complex queries by decomposing the query into individual clues, routing those as sub-queries to specialized retrievers, and ensembling the results.
  arXiv  Detail & Related papers  (2023-05-24T11:43:40Z)
• DAPR: A Benchmark on Document-Aware Passage Retrieval [57.45793782107218]
  We propose and name this task emphDocument-Aware Passage Retrieval (DAPR) While analyzing the errors of the State-of-The-Art (SoTA) passage retrievers, we find the major errors (53.5%) are due to missing document context. Our created benchmark enables future research on developing and comparing retrieval systems for the new task.
  arXiv  Detail & Related papers  (2023-05-23T10:39:57Z)
• CAPSTONE: Curriculum Sampling for Dense Retrieval with Document Expansion [68.19934563919192]
  We propose a curriculum sampling strategy that utilizes pseudo queries during training and progressively enhances the relevance between the generated query and the real query. Experimental results on both in-domain and out-of-domain datasets demonstrate that our approach outperforms previous dense retrieval models.
  arXiv  Detail & Related papers  (2022-12-18T15:57:46Z)
• Decoding a Neural Retriever's Latent Space for Query Suggestion [28.410064376447718]
  We show that it is possible to decode a meaningful query from its latent representation and, when moving in the right direction in latent space, to decode a query that retrieves the relevant paragraph. We employ the query decoder to generate a large synthetic dataset of query reformulations for MSMarco. On this data, we train a pseudo-relevance feedback (PRF) T5 model for the application of query suggestion.
  arXiv  Detail & Related papers  (2022-10-21T16:19:31Z)
• Improving Query Representations for Dense Retrieval with Pseudo Relevance Feedback [29.719150565643965]
  This paper proposes ANCE-PRF, a new query encoder that uses pseudo relevance feedback (PRF) to improve query representations for dense retrieval. ANCE-PRF uses a BERT encoder that consumes the query and the top retrieved documents from a dense retrieval model, ANCE, and it learns to produce better query embeddings directly from relevance labels. Analysis shows that the PRF encoder effectively captures the relevant and complementary information from PRF documents, while ignoring the noise with its learned attention mechanism.
  arXiv  Detail & Related papers  (2021-08-30T18:10:26Z)
• Query Understanding via Intent Description Generation [75.64800976586771]
  We propose a novel Query-to-Intent-Description (Q2ID) task for query understanding. Unlike existing ranking tasks which leverage the query and its description to compute the relevance of documents, Q2ID is a reverse task which aims to generate a natural language intent description. We demonstrate the effectiveness of our model by comparing with several state-of-the-art generation models on the Q2ID task.
  arXiv  Detail & Related papers  (2020-08-25T08:56:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.