@inproceedings{upravitelev-etal-2025-exploring,
title = "Exploring Semantic Filtering Heuristics For Efficient Claim Verification",
author = "Upravitelev, Max and
Sahitaj, Premtim and
Hilbert, Arthur and
Solopova, Veronika and
Yang, Jing and
Feldhus, Nils and
Anikina, Tatiana and
Ostermann, Simon and
Schmitt, Vera",
editor = "Akhtar, Mubashara and
Aly, Rami and
Christodoulopoulos, Christos and
Cocarascu, Oana and
Guo, Zhijiang and
Mittal, Arpit and
Schlichtkrull, Michael and
Thorne, James and
Vlachos, Andreas",
booktitle = "Proceedings of the Eighth Fact Extraction and VERification Workshop (FEVER)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.fever-1.17/",
doi = "10.18653/v1/2025.fever-1.17",
pages = "229--237",
ISBN = "978-1-959429-53-1",
abstract = "Given the limited computational and financial resources of news agencies, real-life usage of fact-checking systems requires fast response times. For this reason, our submission to the FEVER-8 claim verification shared task focuses on optimizing the efficiency of such pipelines built around subtasks such as evidence retrieval and veracity prediction. We propose the Semantic Filtering for Efficient Fact Checking (SFEFC) strategy, which is inspired by the FEVER-8 baseline and designed with the goal of reducing the number of LLM calls and other computationally expensive subroutines. Furthermore, we explore the reuse of cosine similarities initially calculated within a dense retrieval step to retrieve the top 10 most relevant evidence sentence sets. We use these sets for semantic filtering methods based on similarity scores and create filters for particularly hard classification labels ``Not Enough Information'' and ``Conflicting Evidence/Cherrypicking'' by identifying thresholds for potentially relevant information and the semantic variance within these sets. Compared to the parallelized FEVER-8 baseline, which takes 33.88 seconds on average to process a claim according to the FEVER-8 shared task leaderboard, our non-parallelized system remains competitive in regard to AVeriTeC retrieval scores while reducing the runtime to 7.01 seconds, achieving the fastest average runtime per claim."
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<abstract>Given the limited computational and financial resources of news agencies, real-life usage of fact-checking systems requires fast response times. For this reason, our submission to the FEVER-8 claim verification shared task focuses on optimizing the efficiency of such pipelines built around subtasks such as evidence retrieval and veracity prediction. We propose the Semantic Filtering for Efficient Fact Checking (SFEFC) strategy, which is inspired by the FEVER-8 baseline and designed with the goal of reducing the number of LLM calls and other computationally expensive subroutines. Furthermore, we explore the reuse of cosine similarities initially calculated within a dense retrieval step to retrieve the top 10 most relevant evidence sentence sets. We use these sets for semantic filtering methods based on similarity scores and create filters for particularly hard classification labels “Not Enough Information” and “Conflicting Evidence/Cherrypicking” by identifying thresholds for potentially relevant information and the semantic variance within these sets. Compared to the parallelized FEVER-8 baseline, which takes 33.88 seconds on average to process a claim according to the FEVER-8 shared task leaderboard, our non-parallelized system remains competitive in regard to AVeriTeC retrieval scores while reducing the runtime to 7.01 seconds, achieving the fastest average runtime per claim.</abstract>
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%0 Conference Proceedings
%T Exploring Semantic Filtering Heuristics For Efficient Claim Verification
%A Upravitelev, Max
%A Sahitaj, Premtim
%A Hilbert, Arthur
%A Solopova, Veronika
%A Yang, Jing
%A Feldhus, Nils
%A Anikina, Tatiana
%A Ostermann, Simon
%A Schmitt, Vera
%Y Akhtar, Mubashara
%Y Aly, Rami
%Y Christodoulopoulos, Christos
%Y Cocarascu, Oana
%Y Guo, Zhijiang
%Y Mittal, Arpit
%Y Schlichtkrull, Michael
%Y Thorne, James
%Y Vlachos, Andreas
%S Proceedings of the Eighth Fact Extraction and VERification Workshop (FEVER)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 978-1-959429-53-1
%F upravitelev-etal-2025-exploring
%X Given the limited computational and financial resources of news agencies, real-life usage of fact-checking systems requires fast response times. For this reason, our submission to the FEVER-8 claim verification shared task focuses on optimizing the efficiency of such pipelines built around subtasks such as evidence retrieval and veracity prediction. We propose the Semantic Filtering for Efficient Fact Checking (SFEFC) strategy, which is inspired by the FEVER-8 baseline and designed with the goal of reducing the number of LLM calls and other computationally expensive subroutines. Furthermore, we explore the reuse of cosine similarities initially calculated within a dense retrieval step to retrieve the top 10 most relevant evidence sentence sets. We use these sets for semantic filtering methods based on similarity scores and create filters for particularly hard classification labels “Not Enough Information” and “Conflicting Evidence/Cherrypicking” by identifying thresholds for potentially relevant information and the semantic variance within these sets. Compared to the parallelized FEVER-8 baseline, which takes 33.88 seconds on average to process a claim according to the FEVER-8 shared task leaderboard, our non-parallelized system remains competitive in regard to AVeriTeC retrieval scores while reducing the runtime to 7.01 seconds, achieving the fastest average runtime per claim.
%R 10.18653/v1/2025.fever-1.17
%U https://aclanthology.org/2025.fever-1.17/
%U https://doi.org/10.18653/v1/2025.fever-1.17
%P 229-237
Markdown (Informal)
[Exploring Semantic Filtering Heuristics For Efficient Claim Verification](https://aclanthology.org/2025.fever-1.17/) (Upravitelev et al., FEVER 2025)
ACL
- Max Upravitelev, Premtim Sahitaj, Arthur Hilbert, Veronika Solopova, Jing Yang, Nils Feldhus, Tatiana Anikina, Simon Ostermann, and Vera Schmitt. 2025. Exploring Semantic Filtering Heuristics For Efficient Claim Verification. In Proceedings of the Eighth Fact Extraction and VERification Workshop (FEVER), pages 229–237, Vienna, Austria. Association for Computational Linguistics.