QAEncoder: Towards Aligned Representation Learning in Question Answering Systems

• URL: http://arxiv.org/abs/2409.20434v3
• Date: Wed, 02 Jul 2025 15:34:00 GMT
• Title: QAEncoder: Towards Aligned Representation Learning in Question Answering Systems
• Authors: Zhengren Wang, Qinhan Yu, Shida Wei, Zhiyu Li, Feiyu Xiong, Xiaoxing Wang, Simin Niu, Hao Liang, Wentao Zhang,
• Abstract summary: QAEncoder is a training-free approach to bridge the gap between user queries and documents.<n>It estimates the expectation of potential queries in the embedding space as a robust surrogate for the document embedding, and attaches document fingerprints to distinguish these embeddings.<n>It offers a simple-yet-effective solution with zero additional index storage, retrieval latency, training costs, or catastrophic forgetting and hallucination issues.
• Score: 25.283922985211397
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern QA systems entail retrieval-augmented generation (RAG) for accurate and trustworthy responses. However, the inherent gap between user queries and relevant documents hinders precise matching. We introduce QAEncoder, a training-free approach to bridge this gap. Specifically, QAEncoder estimates the expectation of potential queries in the embedding space as a robust surrogate for the document embedding, and attaches document fingerprints to effectively distinguish these embeddings. Extensive experiments across diverse datasets, languages, and embedding models confirmed QAEncoder's alignment capability, which offers a simple-yet-effective solution with zero additional index storage, retrieval latency, training costs, or catastrophic forgetting and hallucination issues. The repository is publicly available at https://github.com/IAAR-Shanghai/QAEncoder.

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