Integrating Chain-of-Thought into Generative Retrieval: A Preliminary Study
Wenhao Zhang, Ruihao Yu, Yi Bai, Zhumin Chen, Pengjie Ren

TL;DR
This paper introduces ThinkGR, a novel framework that integrates chain-of-thought reasoning into generative retrieval, enabling multi-step deliberation and achieving state-of-the-art results on complex retrieval benchmarks.
Contribution
The paper presents a unified approach combining chain-of-thought with generative retrieval, including a hybrid decoding strategy and a two-phase training method, to improve complex query handling.
Findings
Achieves an average of +6.86% improvement on four multi-hop retrieval benchmarks.
Enables iterative thinking and retrieval within a single generative process.
Demonstrates the effectiveness of explicit deliberation in retrieval tasks.
Abstract
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-step reasoning. As a preliminary study on integrating chain-of-thought (CoT) into generative retrieval, we introduce ThinkGR, a unified framework that interleaves CoT with docid generation, enabling iterative thinking and retrieval within a single generative process. To bridge the gap between free-form thought generation and structured retrieval targets, we design (1) a hybrid decoding strategy that dynamically switches between unconstrained thought generation and constrained docid decoding, and (2) a two-phase training approach that first aligns thought-retrieval patterns through supervised fine-tuning, then…
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