Exploring Reasoning-Infused Text Embedding with Large Language Models for Zero-Shot Dense Retrieval

2025-08-29

AI Summary

Existing LLM-based text embedding methods emphasize contextual encoding while neglecting the models’ logical reasoning capabilities, resulting in suboptimal performance on zero-shot dense retrieval tasks requiring complex reasoning. Method: We propose RITE—a novel framework that explicitly integrates the generative large language model’s reasoning ability into the embedding process. RITE enhances raw text semantics by generating intermediate reasoning paths and constructs reasoning-enriched dense vectors directly in token space—without fine-tuning or additional training. Contribution/Results: Evaluated on rigorous reasoning-intensive retrieval benchmarks (e.g., BRIGHT), RITE substantially outperforms state-of-the-art zero-shot embedding methods. It demonstrates strong cross-domain robustness and computational efficiency, establishing a new paradigm for reasoning-augmented information retrieval.

Technology Category

• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Machine Learning: Large Multimodal Models (LMMs)
• Knowledge Representation and Reasoning: Reasoning with Beliefs

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Transformer-based models such as BERT and E5 have significantly advanced text embedding by capturing rich contextual representations. However, many complex real-world queries require sophisticated reasoning to retrieve relevant documents beyond surface-level lexical matching, where encoder-only retrievers often fall short. Decoder-only large language models (LLMs), known for their strong reasoning capabilities, offer a promising alternative. Despite this potential, existing LLM-based embedding methods primarily focus on contextual representation and do not fully exploit the reasoning strength of LLMs. To bridge this gap, we propose Reasoning-Infused Text Embedding (RITE), a simple but effective approach that integrates logical reasoning into the text embedding process using generative LLMs. RITE builds upon existing language model embedding techniques by generating intermediate reasoning texts in the token space before computing embeddings, thereby enriching representations with inferential depth. Experimental results on BRIGHT, a reasoning-intensive retrieval benchmark, demonstrate that RITE significantly enhances zero-shot retrieval performance across diverse domains, underscoring the effectiveness of incorporating reasoning into the embedding process.

Problem

• Enhancing text embeddings with reasoning for retrieval
• Bridging reasoning gaps in LLM-based dense retrieval
• Improving zero-shot retrieval via reasoning-infused embeddings

Innovation

• Integrates logical reasoning into text embedding
• Generates intermediate reasoning texts before computing embeddings
• Enhances zero-shot retrieval with inferential depth