QAEncoder: Towards Aligned Representation Learning in Question Answering System

2024-09-30

arXiv.org

AI Summary

To address semantic mismatch between user queries and documents in RAG systems, this paper proposes a training-free representation alignment method. Grounded in the conical distribution assumption, it estimates the latent query expectation in the embedding space as a proxy representation for documents and augments embeddings with lightweight document fingerprints to improve discriminability. The approach is plug-and-play, compatible with arbitrary embedding models and RAG architectures. Extensive experiments across 6 languages, 8 datasets, and 14 embedding models demonstrate substantial improvements in retrieval accuracy; in end-to-end RAG tasks, Exact Match (EM) and F1 scores increase by 3.2–7.8% on average, with zero training overhead. The core contribution lies in the first application of conical distribution modeling to unsupervised representation alignment—enabling geometrically grounded, language-agnostic, and model-agnostic retrieval enhancement.

Technology Category

• Natural Language Processing: Language Grounding & Multi-modal NLP
• Search and Optimization: Learning to Search
• Machine Learning: Representation Learning

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• 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

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. Motivated by our conical distribution hypothesis, which posits that potential queries and documents form a cone-like structure in the embedding space, 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 on fourteen embedding models across six languages and eight datasets validate QAEncoder's alignment capability, which offers a plug-and-play solution that seamlessly integrates with existing RAG architectures and training-based methods.

Problem

• Bridges gap between user queries and documents
• Enhances alignment in QA representation learning
• Improves matching without training or extra storage

Innovation

• Training-free query-document alignment approach
• Estimates query expectations in embedding space
• Attaches document fingerprints for embedding distinction