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

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.

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