Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.20434 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913922403008512 |
|---|---|
| author | Wang, Zhengren Yu, Qinhan Wei, Shida Li, Zhiyu Xiong, Feiyu Wang, Xiaoxing Niu, Simin Liang, Hao Zhang, Wentao |
| author_facet | Wang, Zhengren Yu, Qinhan Wei, Shida Li, Zhiyu Xiong, Feiyu Wang, Xiaoxing Niu, Simin Liang, Hao Zhang, Wentao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_20434 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | QAEncoder: Towards Aligned Representation Learning in Question Answering Systems Wang, Zhengren Yu, Qinhan Wei, Shida Li, Zhiyu Xiong, Feiyu Wang, Xiaoxing Niu, Simin Liang, Hao Zhang, Wentao Computation and Language 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. |
| title | QAEncoder: Towards Aligned Representation Learning in Question Answering Systems |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2409.20434 |