QAEncoder: Towards Aligned Representation Learning in Question Answering Systems

Understand

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.