Learning More Effective Representations for Dense Retrieval through Deliberate Thinking Before Search

• URL: http://arxiv.org/abs/2502.12974v1
• Date: Tue, 18 Feb 2025 15:56:34 GMT
• Title: Learning More Effective Representations for Dense Retrieval through Deliberate Thinking Before Search
• Authors: Yifan Ji, Zhipeng Xu, Zhenghao Liu, Yukun Yan, Shi Yu, Yishan Li, Zhiyuan Liu, Yu Gu, Ge Yu, Maosong Sun,
• Abstract summary: Deliberate Thinking based Dense Retriever (DEBATER)<n>DEBATER enhances recent dense retrievers by enabling them to learn more effective document representations through a step-by-step thinking process.<n> Experimental results show that DEBATER significantly outperforms existing methods across several retrieval benchmarks.
• Score: 65.53881294642451
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent dense retrievers usually thrive on the emergency capabilities of Large Language Models (LLMs), using them to encode queries and documents into an embedding space for retrieval. These LLM-based dense retrievers have shown promising performance across various retrieval scenarios. However, relying on a single embedding to represent documents proves less effective in capturing different perspectives of documents for matching. In this paper, we propose Deliberate Thinking based Dense Retriever (DEBATER), which enhances these LLM-based retrievers by enabling them to learn more effective document representations through a step-by-step thinking process. DEBATER introduces the Chain-of-Deliberation mechanism to iteratively optimize document representations using a continuous chain of thought. To consolidate information from various thinking steps, DEBATER also incorporates the Self Distillation mechanism, which identifies the most informative thinking steps and integrates them into a unified text embedding. Experimental results show that DEBATER significantly outperforms existing methods across several retrieval benchmarks, demonstrating superior accuracy and robustness. All codes are available at https://github.com/OpenBMB/DEBATER.

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