无需训练即可提升否定查询的检索准确率
DEO: Training-Free Direct Embedding Optimization for Negation-Aware Retrieval
- 将查询拆分为正负成分，用对比目标优化嵌入
- 在NegConstraint上提升nDCG@10达+0.0738，MAP@100提升+0.1028
- 适合需要快速部署、不支持微调的检索系统
大型语言模型和检索增强生成技术推动了多样化检索方法的发展。然而，现有方法在处理否定与排除类查询时表现不佳。以往方案依赖嵌入适配或微调，带来额外计算开销和部署复杂性。本文提出无需训练的直接嵌入优化（DEO），用于否定感知的文本与多模态检索。DEO将查询分解为正负成分，通过对比目标优化查询嵌入。无需额外训练数据或模型更新，在NegConstraint上实现nDCG@10提升+0.0738、MAP@100提升+0.1028；在多模态检索中，召回率@5相比OpenAI CLIP提升6%。结果表明DEO在真实场景下具备实用价值。
原文摘要 · Abstract (English)
Recent advances in Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) have enabled diverse retrieval methods. However, existing retrieval methods often fail to accurately retrieve results for negation and exclusion queries. To address this limitation, prior approaches rely on embedding adaptation or fine-tuning, which introduce additional computational cost and deployment complexity. We propose Direct Embedding Optimization (DEO), a training-free method for negation-aware text and multimodal retrieval. DEO decomposes queries into positive and negative components and optimizes the query embedding with a contrastive objective. Without additional training data or model updates, DEO outperforms baselines on NegConstraint, with gains of +0.0738 nDCG@10 and +0.1028 MAP@100, while improving Recall@5 by +6\% over OpenAI CLIP in multimodal retrieval. These results demonstrate the practicality of DEO for negation- and exclusion-aware retrieval in real-world settings.
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