ClaimIQ at CheckThat! 2025: Comparing Prompted and Fine-Tuned Language
  Models for Verifying Numerical Claims
Authors: name: Anirban Saha Anik, name: Md Fahimul Kabir Chowdhury, name: Andrew Wyckoff, name: Sagnik Ray Choudhury

This paper presents our system for Task 3 of the CLEF 2025 CheckThat! Lab,
which focuses on verifying numerical and temporal claims using retrieved
evidence. We explore two complementary approaches: zero-shot prompting with
instruction-tuned large language models (LLMs) and supervised fine-tuning using
parameter-efficient LoRA. To enhance evidence quality, we investigate several
selection strategies, including full-document input and top-k sentence
filtering using BM25 and MiniLM. Our best-performing model LLaMA fine-tuned
with LoRA achieves strong performance on the English validation set. However, a
notable drop in the test set highlights a generalization challenge. These
findings underscore the importance of evidence granularity and model adaptation
for robust numerical fact verification.