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.
ClaimIQ在CheckThat! 2025：比较基于提示与微调语言模型在验证数值声明中的表现 / ClaimIQ at CheckThat! 2025: Comparing Prompted and Fine-Tuned Language Models for Verifying Numerical Claims
本研究探索了两种不同的AI方法——直接指令调用和精细参数调优，用于验证数字事实声明，发现精细调优的模型在训练数据上表现良好但泛化能力有限，强调了证据处理方式和模型适应对提升验证准确性的关键作用。