@inproceedings{park-etal-2024-dunamu,
title = "Dunamu-ml{'}s Submissions on {AVERITEC} Shared Task",
author = "Park, Heesoo and
Lee, Dongjun and
Kim, Jaehyuk and
Park, ChoongWon and
Park, Changhwa",
editor = "Schlichtkrull, Michael and
Chen, Yulong and
Whitehouse, Chenxi and
Deng, Zhenyun and
Akhtar, Mubashara and
Aly, Rami and
Guo, Zhijiang and
Christodoulopoulos, Christos and
Cocarascu, Oana and
Mittal, Arpit and
Thorne, James and
Vlachos, Andreas",
booktitle = "Proceedings of the Seventh Fact Extraction and VERification Workshop (FEVER)",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.fever-1.7",
doi = "10.18653/v1/2024.fever-1.7",
pages = "71--76",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Dunamu-ml’s Submissions on AVERITEC Shared Task
%A Park, Heesoo
%A Lee, Dongjun
%A Kim, Jaehyuk
%A Park, ChoongWon
%A Park, Changhwa
%Y Schlichtkrull, Michael
%Y Chen, Yulong
%Y Whitehouse, Chenxi
%Y Deng, Zhenyun
%Y Akhtar, Mubashara
%Y Aly, Rami
%Y Guo, Zhijiang
%Y Christodoulopoulos, Christos
%Y Cocarascu, Oana
%Y Mittal, Arpit
%Y Thorne, James
%Y Vlachos, Andreas
%S Proceedings of the Seventh Fact Extraction and VERification Workshop (FEVER)
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F park-etal-2024-dunamu
%X 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.
%R 10.18653/v1/2024.fever-1.7
%U https://aclanthology.org/2024.fever-1.7
%U https://doi.org/10.18653/v1/2024.fever-1.7
%P 71-76
Markdown (Informal)
[Dunamu-ml’s Submissions on AVERITEC Shared Task](https://aclanthology.org/2024.fever-1.7) (Park et al., FEVER 2024)
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.