Automatic Fact-checking in English and Telugu

Ravi Kiran Chikkala; Tatiana Anikina; Natalia Skachkova; Ivan Vykopal; Rodrigo Agerri; Josef van Genabith

Automatic Fact-checking in English and Telugu

Ravi Kiran Chikkala, Tatiana Anikina, Natalia Skachkova, Ivan Vykopal, Rodrigo Agerri, Josef van Genabith

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Abstract

False information poses a significant global challenge, and manually verifying claims is a time-consuming and resource-intensive process. In this research paper, we experiment with different approaches to investigate the effectiveness of large language models (LLMs) in classifying factual claims by their veracity and generating justifications in English and Telugu. The key contributions of this work include the creation of a bilingual English-Telugu dataset and the benchmarking of different veracity classification approaches based on LLMs.

Anthology ID:: 2025.lowresnlp-1.15
Volume:: Proceedings of the First Workshop on Advancing NLP for Low-Resource Languages
Month:: September
Year:: 2025
Address:: Varna, Bulgaria
Editors:: Ernesto Luis Estevanell-Valladares, Alicia Picazo-Izquierdo, Tharindu Ranasinghe, Besik Mikaberidze, Simon Ostermann, Daniil Gurgurov, Philipp Mueller, Claudia Borg, Marián Šimko
Venues:: LowResNLP | WS
SIG:
Publisher:: INCOMA Ltd., Shoumen, Bulgaria
Note:
Pages:: 140–151
Language:
URL:: https://aclanthology.org/2025.lowresnlp-1.15/
DOI:
Bibkey:
Cite (ACL):: Ravi Kiran Chikkala, Tatiana Anikina, Natalia Skachkova, Ivan Vykopal, Rodrigo Agerri, and Josef van Genabith. 2025. Automatic Fact-checking in English and Telugu. In Proceedings of the First Workshop on Advancing NLP for Low-Resource Languages, pages 140–151, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.
Cite (Informal):: Automatic Fact-checking in English and Telugu (Chikkala et al., LowResNLP 2025)
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PDF:: https://aclanthology.org/2025.lowresnlp-1.15.pdf

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@inproceedings{chikkala-etal-2025-automatic,
    title = "Automatic Fact-checking in {E}nglish and {T}elugu",
    author = "Chikkala, Ravi Kiran  and
      Anikina, Tatiana  and
      Skachkova, Natalia  and
      Vykopal, Ivan  and
      Agerri, Rodrigo  and
      van Genabith, Josef",
    editor = "Estevanell-Valladares, Ernesto Luis  and
      Picazo-Izquierdo, Alicia  and
      Ranasinghe, Tharindu  and
      Mikaberidze, Besik  and
      Ostermann, Simon  and
      Gurgurov, Daniil  and
      Mueller, Philipp  and
      Borg, Claudia  and
      {\v{S}}imko, Mari{\'a}n",
    booktitle = "Proceedings of the First Workshop on Advancing NLP for Low-Resource Languages",
    month = sep,
    year = "2025",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd., Shoumen, Bulgaria",
    url = "https://aclanthology.org/2025.lowresnlp-1.15/",
    pages = "140--151",
    abstract = "False information poses a significant global challenge, and manually verifying claims is a time-consuming and resource-intensive process. In this research paper, we experiment with different approaches to investigate the effectiveness of large language models (LLMs) in classifying factual claims by their veracity and generating justifications in English and Telugu. The key contributions of this work include the creation of a bilingual English-Telugu dataset and the benchmarking of different veracity classification approaches based on LLMs."
}

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%0 Conference Proceedings
%T Automatic Fact-checking in English and Telugu
%A Chikkala, Ravi Kiran
%A Anikina, Tatiana
%A Skachkova, Natalia
%A Vykopal, Ivan
%A Agerri, Rodrigo
%A van Genabith, Josef
%Y Estevanell-Valladares, Ernesto Luis
%Y Picazo-Izquierdo, Alicia
%Y Ranasinghe, Tharindu
%Y Mikaberidze, Besik
%Y Ostermann, Simon
%Y Gurgurov, Daniil
%Y Mueller, Philipp
%Y Borg, Claudia
%Y Šimko, Marián
%S Proceedings of the First Workshop on Advancing NLP for Low-Resource Languages
%D 2025
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F chikkala-etal-2025-automatic
%X False information poses a significant global challenge, and manually verifying claims is a time-consuming and resource-intensive process. In this research paper, we experiment with different approaches to investigate the effectiveness of large language models (LLMs) in classifying factual claims by their veracity and generating justifications in English and Telugu. The key contributions of this work include the creation of a bilingual English-Telugu dataset and the benchmarking of different veracity classification approaches based on LLMs.
%U https://aclanthology.org/2025.lowresnlp-1.15/
%P 140-151

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Markdown (Informal)

[Automatic Fact-checking in English and Telugu](https://aclanthology.org/2025.lowresnlp-1.15/) (Chikkala et al., LowResNLP 2025)

Automatic Fact-checking in English and Telugu (Chikkala et al., LowResNLP 2025)

ACL

Ravi Kiran Chikkala, Tatiana Anikina, Natalia Skachkova, Ivan Vykopal, Rodrigo Agerri, and Josef van Genabith. 2025. Automatic Fact-checking in English and Telugu. In Proceedings of the First Workshop on Advancing NLP for Low-Resource Languages, pages 140–151, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.