ClaimIQ at CheckThat! 2025: Comparing Prompted and Fine-Tuned Language
  Models for Verifying Numerical Claims
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.
68c9dc5e6e0073c09bd1dd5e
Anirban Saha Anik
claimed_verified
2025-09-17T13:05:20.279Z
66627df0a7cd190bd5a3bc51
/avatars/517076f9f2fcb0a01c04a53a326646b1.svg
Anirban Saha Anik
user
AnirbanSaha
68c9dc5e6e0073c09bd1dd5f
Md Fahimul Kabir Chowdhury
68c9dc5e6e0073c09bd1dd60
Andrew Wyckoff
68c9dc5e6e0073c09bd1dd61
Sagnik Ray Choudhury
ClaimIQ at CheckThat! 2025: Comparing Prompted and Fine-Tuned Language
  Models for Verifying Numerical Claims
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.
The system uses zero-shot prompting and parameter-efficient fine-tuning to verify numerical and temporal claims, with findings highlighting the importance of evidence granularity and model adaptation.
zero-shot prompting instruction-tuned large language models supervised fine-tuning parameter-efficient LoRA full-document input top-k sentence filtering BM25 MiniLM LLaMA numerical fact verification