Synergistic Interplay between Search and Large Language Models for Information Retrieval

• URL: http://arxiv.org/abs/2305.07402v3
• Date: Tue, 12 Dec 2023 14:04:34 GMT
• Title: Synergistic Interplay between Search and Large Language Models for Information Retrieval
• Authors: Jiazhan Feng, Chongyang Tao, Xiubo Geng, Tao Shen, Can Xu, Guodong Long, Dongyan Zhao, Daxin Jiang
• Abstract summary: 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.
• Score: 141.18083677333848
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Information retrieval (IR) plays a crucial role in locating relevant resources from vast amounts of data, and its applications have evolved from traditional knowledge bases to modern retrieval models (RMs). The emergence of large language models (LLMs) has further revolutionized the IR field by enabling users to interact with search systems in natural languages. In this paper, we explore the advantages and disadvantages of LLMs and RMs, highlighting their respective strengths in understanding user-issued queries and retrieving up-to-date information. To leverage the benefits of both paradigms while circumventing their limitations, we propose InteR, a novel framework that facilitates information refinement through synergy between RMs and LLMs. InteR allows RMs to expand knowledge in queries using LLM-generated knowledge collections and enables LLMs to enhance prompt formulation using retrieved documents. This iterative refinement process augments the inputs of RMs and LLMs, leading to more accurate retrieval. Experiments on large-scale retrieval benchmarks involving web search and low-resource retrieval tasks demonstrate that InteR achieves overall superior zero-shot retrieval performance compared to state-of-the-art methods, even those using relevance judgment. Source code is available at https://github.com/Cyril-JZ/InteR

Related papers

• Towards Enhancing Linked Data Retrieval in Conversational UIs using Large Language Models [1.3980986259786221]
  This paper examines the integration of Large Language Models (LLMs) within existing systems. By leveraging the advanced natural language understanding capabilities of LLMs, our method improves RDF entity extraction within web systems. The evaluation of this methodology shows a marked enhancement in system expressivity and the accuracy of responses to user queries.
  arXiv  Detail & Related papers  (2024-09-24T16:31:33Z)
• Redefining Information Retrieval of Structured Database via Large Language Models [9.65171883231521]
  This paper introduces a novel retrieval augmentation framework called ChatLR. It primarily employs the powerful semantic understanding ability of Large Language Models (LLMs) as retrievers to achieve precise and concise information retrieval. Experimental results demonstrate the effectiveness of ChatLR in addressing user queries, achieving an overall information retrieval accuracy exceeding 98.8%.
  arXiv  Detail & Related papers  (2024-05-09T02:37:53Z)
• Unsupervised Information Refinement Training of Large Language Models for Retrieval-Augmented Generation [128.01050030936028]
  We propose an information refinement training method named InFO-RAG. InFO-RAG is low-cost and general across various tasks. It improves the performance of LLaMA2 by an average of 9.39% relative points.
  arXiv  Detail & Related papers  (2024-02-28T08:24:38Z)
• REAR: A Relevance-Aware Retrieval-Augmented Framework for Open-Domain Question Answering [122.62012375722124]
  In existing methods, large language models (LLMs) cannot precisely assess the relevance of retrieved documents. We propose REAR, a RElevance-Aware Retrieval-augmented approach for open-domain question answering (QA)
  arXiv  Detail & Related papers  (2024-02-27T13:22:51Z)
• Self-Retrieval: End-to-End Information Retrieval with One Large Language Model [97.71181484082663]
  We introduce Self-Retrieval, a novel end-to-end LLM-driven information retrieval architecture. Self-Retrieval internalizes the retrieval corpus through self-supervised learning, transforms the retrieval process into sequential passage generation, and performs relevance assessment for reranking.
  arXiv  Detail & Related papers  (2024-02-23T18:45:35Z)
• ReSLLM: Large Language Models are Strong Resource Selectors for Federated Search [35.44746116088232]
  Federated search will become increasingly pivotal in the context of Retrieval-Augmented Generation pipelines. Current SOTA resource selection methodologies rely on feature-based learning approaches. We propose ReSLLM to drive the selection of resources in federated search in a zero-shot setting.
  arXiv  Detail & Related papers  (2024-01-31T07:58:54Z)
• Context Matters: Pushing the Boundaries of Open-Ended Answer Generation with Graph-Structured Knowledge Context [4.1229332722825]
  This paper introduces a novel framework that combines graph-driven context retrieval in conjunction to knowledge graphs based enhancement. We conduct experiments on various Large Language Models (LLMs) with different parameter sizes to evaluate their ability to ground knowledge and determine factual accuracy in answers to open-ended questions. Our methodology GraphContextGen consistently outperforms dominant text-based retrieval systems, demonstrating its robustness and adaptability to a larger number of use cases.
  arXiv  Detail & Related papers  (2024-01-23T11:25:34Z)
• Large Language Models for Information Retrieval: A Survey [58.30439850203101]
  Information retrieval has evolved from term-based methods to its integration with advanced neural models. Recent research has sought to leverage large language models (LLMs) to improve IR systems. We delve into the confluence of LLMs and IR systems, including crucial aspects such as query rewriters, retrievers, rerankers, and readers.
  arXiv  Detail & Related papers  (2023-08-14T12:47:22Z)
• RRAML: Reinforced Retrieval Augmented Machine Learning [10.94680155282906]
  We propose a novel framework called Reinforced Retrieval Augmented Machine Learning (RRAML) RRAML integrates the reasoning capabilities of large language models with supporting information retrieved by a purpose-built retriever from a vast user-provided database. We believe that the research agenda outlined in this paper has the potential to profoundly impact the field of AI.
  arXiv  Detail & Related papers  (2023-07-24T13:51:19Z)
• Search-in-the-Chain: Interactively Enhancing Large Language Models with Search for Knowledge-intensive Tasks [121.74957524305283]
  This paper proposes a novel framework named textbfSearch-in-the-Chain (SearChain) for the interaction between Information Retrieval (IR) and Large Language Model (LLM) Experiments show that SearChain outperforms state-of-the-art baselines on complex knowledge-intensive tasks.
  arXiv  Detail & Related papers  (2023-04-28T10:15:25Z)

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.