Towards Efficient Self-Explainable Climate-Related Claim Verification with Generative Models

Siting Liang; Omar Adjali; Daniel Sonntag

doi:10.63317/5kn3aoyqc7rx

Towards Efficient Self-Explainable Climate-Related Claim Verification with Generative Models

Siting Liang, Omar Adjali, Daniel Sonntag

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Abstract

In this work, we present an empirical investigation into two self-explanatory inference paradigms using pre-trained language models with different sizes, based on our participation in the ClimateCheck@NSLP 2026 shared task on climate-related claim verification. This task aims to address the increasing amount of climate misinformation and disinformation on social media, emphasizing the importance of basing claims on reliable scientific evidence. Our study investigates the impact of different explanation strategies on entailment-based verification performance in scientific claim verification, while analyzing the trade-off between reasoning complexity and computational efficiency.

Anthology ID:: 2026.nslp-1.25
Volume:: Proceedings of Natural Scientific Language Processing (NSLP) @ LREC 2026
Month:: May
Year:: 2026
Address:: Palma, Mallorca (Spain)
Editors:: Georg Rehm, Stefan Dietze, Danilo Dessi, Diana Maynard, Sonja Schimmler
Venues:: NSLP | WS
SIG:
Publisher:: ELRA Language Resources Association (ELRA)
Note:
Pages:: 255–260
Language:
External URL:: https://lrec.elra.info/lrec2026-ws-nslp-25
DOI:: 10.63317/5kn3aoyqc7rx
Bibkey:
Cite (ACL):: Siting Liang, Omar Adjali, and Daniel Sonntag. 2026. Towards Efficient Self-Explainable Climate-Related Claim Verification with Generative Models. In Proceedings of Natural Scientific Language Processing (NSLP) @ LREC 2026, pages 255–260, Palma, Mallorca (Spain). ELRA Language Resources Association (ELRA).
Cite (Informal):: Towards Efficient Self-Explainable Climate-Related Claim Verification with Generative Models (Liang et al., NSLP 2026)
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@inproceedings{liang-etal-2026-towards,
    title = "Towards Efficient Self-Explainable Climate-Related Claim Verification with Generative Models",
    author = "Liang, Siting  and
      Adjali, Omar  and
      Sonntag, Daniel",
    editor = "Rehm, Georg  and
      Dietze, Stefan  and
      Dessi, Danilo  and
      Maynard, Diana  and
      Schimmler, Sonja",
    booktitle = "Proceedings of Natural Scientific Language Processing ({NSLP}) @ {LREC} 2026",
    month = may,
    year = "2026",
    address = "Palma, Mallorca (Spain)",
    publisher = "ELRA Language Resources Association (ELRA)",
    url = "https://aclanthology.org/2026.nslp-1.25/",
    doi = "10.63317/5kn3aoyqc7rx",
    pages = "255--260",
    abstract = "In this work, we present an empirical investigation into two self-explanatory inference paradigms using pre-trained language models with different sizes, based on our participation in the \textbf{ClimateCheck@NSLP 2026} shared task on climate-related claim verification. This task aims to address the increasing amount of climate misinformation and disinformation on social media, emphasizing the importance of basing claims on reliable scientific evidence. Our study investigates the impact of different explanation strategies on entailment-based verification performance in scientific claim verification, while analyzing the trade-off between reasoning complexity and computational efficiency."
}

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%0 Conference Proceedings
%T Towards Efficient Self-Explainable Climate-Related Claim Verification with Generative Models
%A Liang, Siting
%A Adjali, Omar
%A Sonntag, Daniel
%Y Rehm, Georg
%Y Dietze, Stefan
%Y Dessi, Danilo
%Y Maynard, Diana
%Y Schimmler, Sonja
%S Proceedings of Natural Scientific Language Processing (NSLP) @ LREC 2026
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F liang-etal-2026-towards
%X In this work, we present an empirical investigation into two self-explanatory inference paradigms using pre-trained language models with different sizes, based on our participation in the ClimateCheck@NSLP 2026 shared task on climate-related claim verification. This task aims to address the increasing amount of climate misinformation and disinformation on social media, emphasizing the importance of basing claims on reliable scientific evidence. Our study investigates the impact of different explanation strategies on entailment-based verification performance in scientific claim verification, while analyzing the trade-off between reasoning complexity and computational efficiency.
%R 10.63317/5kn3aoyqc7rx
%U https://aclanthology.org/2026.nslp-1.25/
%U https://doi.org/10.63317/5kn3aoyqc7rx
%P 255-260

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Markdown (Informal)

[Towards Efficient Self-Explainable Climate-Related Claim Verification with Generative Models](https://aclanthology.org/2026.nslp-1.25/) (Liang et al., NSLP 2026)

Towards Efficient Self-Explainable Climate-Related Claim Verification with Generative Models (Liang et al., NSLP 2026)

ACL

Siting Liang, Omar Adjali, and Daniel Sonntag. 2026. Towards Efficient Self-Explainable Climate-Related Claim Verification with Generative Models. In Proceedings of Natural Scientific Language Processing (NSLP) @ LREC 2026, pages 255–260, Palma, Mallorca (Spain). ELRA Language Resources Association (ELRA).