Retrieval-augmented Generation across Heterogeneous Knowledge

Wenhao Yu


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
Bibkey:
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)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/2022.naacl-srw.7.pdf
Video:
 https://preview.aclanthology.org/ingestion-script-update/2022.naacl-srw.7.mp4
Data
CREAKNatural Questions