Abstract
Retrieval-augmented generation (RAG) methods have been receiving increasing attention from the NLP community and achieved state-of-the-art performance on many NLP downstream tasks. Compared with conventional pre-trained generation models, RAG methods have remarkable advantages such as easy knowledge acquisition, strong scalability, and low training cost. Although existing RAG models have been applied to various knowledge-intensive NLP tasks, such as open-domain QA and dialogue systems, most of the work has focused on retrieving unstructured text documents from Wikipedia. In this paper, I first elaborate on the current obstacles to retrieving knowledge from a single-source homogeneous corpus. Then, I demonstrate evidence from both existing literature and my experiments, and provide multiple solutions on retrieval-augmented generation methods across heterogeneous knowledge.- Anthology ID:
- 2022.naacl-srw.7
- Volume:
- Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop
- Month:
- July
- Year:
- 2022
- Address:
- Hybrid: Seattle, Washington + Online
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 52–58
- Language:
- URL:
- https://aclanthology.org/2022.naacl-srw.7
- DOI:
- 10.18653/v1/2022.naacl-srw.7
- Cite (ACL):
- Wenhao Yu. 2022. Retrieval-augmented Generation across Heterogeneous Knowledge. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop, pages 52–58, Hybrid: Seattle, Washington + Online. Association for Computational Linguistics.
- Cite (Informal):
- Retrieval-augmented Generation across Heterogeneous Knowledge (Yu, NAACL 2022)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.naacl-srw.7.pdf
- Data
- CREAK, Natural Questions