Let Retrievers Think Before Action: Thought-Augmented Embedding for Dense Retrieval

Abstract

Large language models (LLMs) have demonstrated that explicitly performing step-by-step thinking before producing final outputs can substantially improve performance on complex tasks, as exemplified by recent reasoning-oriented models such as OpenAI O1 and DeepSeek R1. Inspired by these advancements, we propose the O1 Embedder, a novel approach aiming to endow retrieval models with similar capabilities to address challenges like multi-task retrieval, zero-shot retrieval, and tasks requiring intensive reasoning of complex relationships. The O1 Embedder generates preliminary thoughts for input queries before document retrieval. To realize this objec-tive, we address two fundamental challenges in integrating thinking mechanisms into dense retrieval. First, retrieval tasks lack explicit supervision for intermediate thinking processes, making it difficult to define thoughts that are truly useful for retrieval. We address this challenge with a data synthesis framework following an Exploration-Refinement process, ensuring alignment with retrieval utility. Second, effectively integrating thought generation with representation learning requires a uni-fied modeling framework that can jointly support generation and embedding within a single model. O1 Embedder addresses this challenge by jointly