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ThinkGR framework enhances generative retrieval with Chain-of-Thought reasoning

By PulseAugur Editorial · [1 sources] ·

Researchers have developed ThinkGR, a novel framework that integrates Chain-of-Thought (CoT) reasoning into generative retrieval systems. This approach allows for iterative thinking and document identification within a single generative process, addressing limitations in handling complex, multi-step queries. ThinkGR employs a hybrid decoding strategy and a two-phase training method to bridge free-form thought generation with structured retrieval targets. Experiments show ThinkGR achieves state-of-the-art results on four multi-hop retrieval benchmarks, with an average performance improvement of 6.86%. AI

IMPACT Enhances retrieval systems for complex queries, potentially improving search accuracy in knowledge-intensive domains.

RANK_REASON The cluster contains a research paper detailing a new framework and experimental results. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

ThinkGR framework enhances generative retrieval with Chain-of-Thought reasoning

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The cluster contains a research paper detailing a new framework and experimental results. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Pengjie Ren ·

    Integrating Chain-of-Thought into Generative Retrieval: A Preliminary Study

    While generative retrieval (GR) demonstrates competitive performance on standard retrieval benchmarks, existing approaches directly map queries to document identifiers (docids) without intermediate deliberation, limiting their effectiveness for complex queries that require multi-…

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