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

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) →

• arXiv
• Chain-of-Thought
• ThinkGR
• Generative Retrieval

• paper
• model release

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