DS@GT ARC at CheckThat! 2026: LLM-Based Trace Ranking and Grouped Reward Modeling for Multilingual Numerical Claim Verification
This paper describes a system for CLEF 2026 CheckThat! Task 2, focusing on automated verification of numerical claims in English and Arabic. Two approaches are explored: an LLM-based verifier fine-tuned with LoRA, and a lightweight TF-IDF reward model. Results show the LLM approach outperforms on most metrics, especially Recall@5, while the reward model excels on conflicting claims. Sub-claim decomposition did not improve performance. For Arabic, AraBERT outperforms multilingual baselines.
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[Submitted on 27 Jul 2026]
Title:DS@GT ARC at CheckThat! 2026: LLM-Based Trace Ranking and Grouped Reward Modeling for Multilingual Numerical Claim Verification
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Abstract:Automated verification of numerical claims is a challenging problem, as it requires both language understanding and quantitative reasoning. This paper describes our system for CLEF 2026 CheckThat! Task 2, which focuses on ranking reasoning traces generated by large language models (LLMs) and predicting a final verdict for numerical claims in English and Arabic. We explore two approaches. The first approach fine-tunes an LLM-based verifier using LoRA to score each reasoning trace independently as a binary classification problem, and selects the final verdict using Best-of-N selection. We further experiment with adaptive sub-claim decomposition to break complex claims into simpler parts before verification. The second approach uses a lightweight TF-IDF reward model with handcrafted numeric and temporal overlap features to score traces, and aggregates scores by verdict group to determine the final prediction. For Arabic, we compare a general multilingual model against AraBERT, a language-specific model pretrained on Arabic text. Our results show that the LLM-based approach outperforms the lightweight reward model on most metrics, particularly Recall@5, while the reward-based approach shows stronger performance on the Conflicting class. Sub-claim decomposition did not improve performance, suggesting that claim splitting introduces noise rather than aiding reasoning. For Arabic, AraBERT outperforms the multilingual baseline across most metrics.
Comments: 10 pages, 2 figures. Accepted at CLEF 2026 CheckThat!. To appear in CEUR Workshop Proceedings
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.25069 [cs.CL]
(or arXiv:2607.25069v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.25069
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Sagnik Sinha [view email] [v1] Mon, 27 Jul 2026 20:59:14 UTC (159 KB)
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