@inproceedings{upravitelev-etal-2025-comparing,
title = "Comparing {LLM}s and {BERT}-based Classifiers for Resource-Sensitive Claim Verification in Social Media",
author = "Upravitelev, Max and
Duran-Silva, Nicolau and
Woerle, Christian and
Guarino, Giuseppe and
Mohtaj, Salar and
Yang, Jing and
Solopova, Veronika and
Schmitt, Vera",
editor = "Ghosal, Tirthankar and
Mayr, Philipp and
Singh, Amanpreet and
Naik, Aakanksha and
Rehm, Georg and
Freitag, Dayne and
Li, Dan and
Schimmler, Sonja and
De Waard, Anita",
booktitle = "Proceedings of the Fifth Workshop on Scholarly Document Processing (SDP 2025)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.sdp-1.26/",
doi = "10.18653/v1/2025.sdp-1.26",
pages = "281--287",
ISBN = "979-8-89176-265-7",
abstract = "The overwhelming volume of content being published at any given moment poses a significant challenge for the design of automated fact-checking (AFC) systems on social media, requiring an emphasized consideration of efficiency aspects.As in other fields, systems built upon LLMs have achieved good results on different AFC benchmarks. However, the application of LLMs is accompanied by high resource requirements. The energy consumption of LLMs poses a significant challenge from an ecological perspective, while remaining a bottleneck in latency-sensitive scenarios like AFC within social media. Therefore, we propose a system built upon fine-tuned smaller BERT-based models. When evaluated on the ClimateCheck dataset against decoder-only LLMs, our best fine-tuned model outperforms Phi 4 and approaches Qwen3 14B in reasoning mode {---} while significantly reducing runtime per claim. Our findings demonstrate that small encoder-only models fine-tuned for specific tasks can still provide a substantive alternative to large decoder-only LLMs, especially in efficiency-concerned settings."
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%0 Conference Proceedings
%T Comparing LLMs and BERT-based Classifiers for Resource-Sensitive Claim Verification in Social Media
%A Upravitelev, Max
%A Duran-Silva, Nicolau
%A Woerle, Christian
%A Guarino, Giuseppe
%A Mohtaj, Salar
%A Yang, Jing
%A Solopova, Veronika
%A Schmitt, Vera
%Y Ghosal, Tirthankar
%Y Mayr, Philipp
%Y Singh, Amanpreet
%Y Naik, Aakanksha
%Y Rehm, Georg
%Y Freitag, Dayne
%Y Li, Dan
%Y Schimmler, Sonja
%Y De Waard, Anita
%S Proceedings of the Fifth Workshop on Scholarly Document Processing (SDP 2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-265-7
%F upravitelev-etal-2025-comparing
%X The overwhelming volume of content being published at any given moment poses a significant challenge for the design of automated fact-checking (AFC) systems on social media, requiring an emphasized consideration of efficiency aspects.As in other fields, systems built upon LLMs have achieved good results on different AFC benchmarks. However, the application of LLMs is accompanied by high resource requirements. The energy consumption of LLMs poses a significant challenge from an ecological perspective, while remaining a bottleneck in latency-sensitive scenarios like AFC within social media. Therefore, we propose a system built upon fine-tuned smaller BERT-based models. When evaluated on the ClimateCheck dataset against decoder-only LLMs, our best fine-tuned model outperforms Phi 4 and approaches Qwen3 14B in reasoning mode — while significantly reducing runtime per claim. Our findings demonstrate that small encoder-only models fine-tuned for specific tasks can still provide a substantive alternative to large decoder-only LLMs, especially in efficiency-concerned settings.
%R 10.18653/v1/2025.sdp-1.26
%U https://aclanthology.org/2025.sdp-1.26/
%U https://doi.org/10.18653/v1/2025.sdp-1.26
%P 281-287
Markdown (Informal)
[Comparing LLMs and BERT-based Classifiers for Resource-Sensitive Claim Verification in Social Media](https://aclanthology.org/2025.sdp-1.26/) (Upravitelev et al., sdp 2025)
ACL
- Max Upravitelev, Nicolau Duran-Silva, Christian Woerle, Giuseppe Guarino, Salar Mohtaj, Jing Yang, Veronika Solopova, and Vera Schmitt. 2025. Comparing LLMs and BERT-based Classifiers for Resource-Sensitive Claim Verification in Social Media. In Proceedings of the Fifth Workshop on Scholarly Document Processing (SDP 2025), pages 281–287, Vienna, Austria. Association for Computational Linguistics.