NapierNLP at CheckThat! 2025: Detecting Subjectivity with LLMs and Model Fusion Notebook for the CheckThat! Lab at CLEF 2025
Year: 2025
Type: conference-paper
Abstract: This paper presents the participation of our team, NapierNLP, in the CLEF2025 CheckThat! Lab Task 1: Subjectivity. This task aimed to differentiate between subjective and objective sentences within a corpus sourced from news articles. We formulate the task as a classification problem and fine-tune three pre-trained large language models (LLMs): GPT2, Qwen, and GPT-Neo. To enhance our predictions, we combine the outputs from multiple models using a majority voting method. We conducted experiments and evaluated our approach using the CheckThat! 2025 Task 1 dataset. Our results showed that GPT-Neo outperforms the other two models. In the official competition results, we ranked 18th in the monolingual English category, but our combination method proved to be more effective than the individual models we employed.
Authors: Alexander, Katarina, Ullah, Zia, Gkatzia, Dimitra
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7113260271
FWCI:
Cites:
Cited by:
Related to: