STaRK: Benchmarking LLM Retrieval on Textual and Relational Knowledge Bases

• URL: http://arxiv.org/abs/2404.13207v3
• Date: Sun, 20 Oct 2024 18:59:02 GMT
• Title: STaRK: Benchmarking LLM Retrieval on Textual and Relational Knowledge Bases
• Authors: Shirley Wu, Shiyu Zhao, Michihiro Yasunaga, Kexin Huang, Kaidi Cao, Qian Huang, Vassilis N. Ioannidis, Karthik Subbian, James Zou, Jure Leskovec,
• Abstract summary: 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.
• Score: 93.96463520716759
• License:
• Abstract: Answering real-world complex queries, such as complex product search, often requires accurate retrieval from semi-structured knowledge bases that involve blend of unstructured (e.g., textual descriptions of products) and structured (e.g., entity relations of products) information. However, many previous works studied textual and relational retrieval tasks as separate topics. To address the gap, we develop STARK, a large-scale Semi-structure retrieval benchmark on Textual and Relational 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, together with their ground-truth answers (items). We conduct rigorous human evaluation to validate the quality of our synthesized queries. We further enhance the benchmark with high-quality human-generated queries to provide an authentic reference. STARK serves as a comprehensive testbed for evaluating the performance of retrieval systems driven by large language models (LLMs). Our experiments suggest that STARK presents significant challenges to the current retrieval and LLM systems, highlighting the need for more capable semi-structured retrieval systems. The benchmark data and code are available on https://github.com/snap-stanford/STaRK.

Related papers

• RiTeK: A Dataset for Large Language Models Complex Reasoning over Textual Knowledge Graphs [12.846097618151951]
  We develop a dataset for LLMs Complex Reasoning over Textual Knowledge Graphs (RiTeK) with a broad topological structure coverage. We synthesize realistic user queries that integrate diverse topological structures, annotated information, and complex textual descriptions. We introduce an enhanced Monte Carlo Tree Search (CTS) method, which automatically extracts relational path information from textual graphs for specific queries.
  arXiv  Detail & Related papers  (2024-10-17T19:33:37Z)
• Knowledge-Aware Query Expansion with Large Language Models for Textual and Relational Retrieval [49.42043077545341]
  We propose a knowledge-aware query expansion framework, augmenting LLMs with structured document relations from knowledge graph (KG) We leverage document texts as rich KG node representations and use document-based relation filtering for our Knowledge-Aware Retrieval (KAR)
  arXiv  Detail & Related papers  (2024-10-17T17:03:23Z)
• CoIR: A Comprehensive Benchmark for Code Information Retrieval Models [56.691926887209895]
  We present textbfname (textbfInformation textbfRetrieval Benchmark), a robust and comprehensive benchmark specifically designed to assess code retrieval capabilities. name comprises textbften meticulously curated code datasets, spanning textbfeight distinctive retrieval tasks across textbfseven diverse domains. We evaluate nine widely used retrieval models using name, uncovering significant difficulties in performing code retrieval tasks even with state-of-the-art systems.
  arXiv  Detail & Related papers  (2024-07-03T07:58:20Z)
• BIRCO: A Benchmark of Information Retrieval Tasks with Complex Objectives [2.3420045370973828]
  We present the Benchmark of Information Retrieval (IR) tasks with Complex Objectives (BIRCO) BIRCO evaluates the ability of IR systems to retrieve documents given multi-faceted user objectives.
  arXiv  Detail & Related papers  (2024-02-21T22:22:30Z)
• DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain Question Answering over Knowledge Base and Text [73.68051228972024]
  Large Language Models (LLMs) have exhibited impressive generation capabilities, but they suffer from hallucinations when relying on their internal knowledge. Retrieval-augmented LLMs have emerged as a potential solution to ground LLMs in external knowledge.
  arXiv  Detail & Related papers  (2023-10-31T04:37:57Z)
• Large Search Model: Redefining Search Stack in the Era of LLMs [63.503320030117145]
  We introduce a novel conceptual framework called large search model, which redefines the conventional search stack by unifying search tasks with one large language model (LLM) All tasks are formulated as autoregressive text generation problems, allowing for the customization of tasks through the use of natural language prompts. This proposed framework capitalizes on the strong language understanding and reasoning capabilities of LLMs, offering the potential to enhance search result quality while simultaneously simplifying the existing cumbersome search stack.
  arXiv  Detail & Related papers  (2023-10-23T05:52:09Z)
• Synergistic Interplay between Search and Large Language Models for Information Retrieval [141.18083677333848]
  InteR allows RMs to expand knowledge in queries using LLM-generated knowledge collections. InteR achieves overall superior zero-shot retrieval performance compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-12T11:58:15Z)

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.