Improving Retrieval Augmented Open-Domain Question-Answering with Vectorized Contexts
Zhuo Chen, Xinyu Wang, Yong Jiang, Pengjun Xie, Fei Huang, Kewei Tu
Abstract
In the era of large language models, applying techniques such as Retrieval Augmented Generation can better address Open-Domain Question-Answering problems. Due to constraints including model sizes and computing resources, the length of context is often limited, and it becomes challenging to empower the model to cover overlong contexts while answering questions from open domains. This paper proposes a general and convenient method to cover longer contexts in Open-Domain Question-Answering tasks. %It leverages a small encoder language model that effectively encodes contexts, and the encoding applies cross-attention with origin inputs.It leverages a small encoder and cross-attention mechanism and effectively encodes contexts. With our method, the original language models can cover several times longer contexts while keeping the computing requirements close to the baseline. Our experiments demonstrate that after fine-tuning, there is improved performance across two held-in datasets, four held-out datasets, and also in two In Context Learning settings. Our code will be released at https://github.com/Alibaba-NLP/Vec-RA-ODQA.- Anthology ID:
- 2024.findings-acl.458
- Volume:
- Findings of the Association for Computational Linguistics ACL 2024
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand and virtual meeting
- Editors:
- Lun-Wei Ku, Andre Martins, Vivek Srikumar
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 7683–7694
- Language:
- URL:
- https://aclanthology.org/2024.findings-acl.458
- DOI:
- Cite (ACL):
- Zhuo Chen, Xinyu Wang, Yong Jiang, Pengjun Xie, Fei Huang, and Kewei Tu. 2024. Improving Retrieval Augmented Open-Domain Question-Answering with Vectorized Contexts. In Findings of the Association for Computational Linguistics ACL 2024, pages 7683–7694, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.
- Cite (Informal):
- Improving Retrieval Augmented Open-Domain Question-Answering with Vectorized Contexts (Chen et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.findings-acl.458.pdf