Dunamu-ml’s Submissions on AVERITEC Shared Task

Heesoo Park, Dongjun Lee, Jaehyuk Kim, ChoongWon Park, Changhwa Park


Abstract
This paper presents the Dunamu-ml’s submission to the AVERITEC shared task of the 7th the Fact Extraction and VERification (FEVER) workshop. The task focused on discriminating whether each claim is a fact or not. Our method is powered by the combination of an LLM and a non-parametric lexicon-based method (i.e. BM25). Essentially, we augmented the list of evidences containing the query and the corresponding answers using an powerful LLM, then, retrieved the relative documents using the generated evidences. As such, our method made a great improvement over the baseline results, achieving 0.33 performance gain over the baseline in AveriTec score.
Anthology ID:
2024.fever-1.7
Volume:
Proceedings of the Seventh Fact Extraction and VERification Workshop (FEVER)
Month:
November
Year:
2024
Address:
Miami, Florida, USA
Editors:
Michael Schlichtkrull, Yulong Chen, Chenxi Whitehouse, Zhenyun Deng, Mubashara Akhtar, Rami Aly, Zhijiang Guo, Christos Christodoulopoulos, Oana Cocarascu, Arpit Mittal, James Thorne, Andreas Vlachos
Venue:
FEVER
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
71–76
Language:
URL:
https://aclanthology.org/2024.fever-1.7
DOI:
10.18653/v1/2024.fever-1.7
Bibkey:
Cite (ACL):
Heesoo Park, Dongjun Lee, Jaehyuk Kim, ChoongWon Park, and Changhwa Park. 2024. Dunamu-ml’s Submissions on AVERITEC Shared Task. In Proceedings of the Seventh Fact Extraction and VERification Workshop (FEVER), pages 71–76, Miami, Florida, USA. Association for Computational Linguistics.
Cite (Informal):
Dunamu-ml’s Submissions on AVERITEC Shared Task (Park et al., FEVER 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.fever-1.7.pdf