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

ACL ARR 2026 January Submission5745 Authors

Keywords: Generative Retrieval, Multi-hop Retrieval, Chain-of-Thought

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 optimizes thought quality via retrieval-grounded reinforcement learning.
Experiments on four multi-hop retrieval benchmarks demonstrate that ThinkGR achieves state-of-the-art performance with an average improvement of +6.86\%.
Our work opens new avenues for enhancing generative retrieval with explicit deliberation capabilities, with promising implications for retrieval tasks requiring complex reasoning.

Paper Type: Long

Research Area: Information Extraction and Retrieval

Research Area Keywords: Information Retrieval and Text Mining

Contribution Types: NLP engineering experiment

Languages Studied: English

Submission Number: 5745