NapierNLP at CheckThat! 2025: Detecting Subjectivity with LLMs and Model Fusion Notebook for the CheckThat! Lab at CLEF 2025

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.

Publication Details

Journal
Edinburgh Napier Research Repository (Edinburgh Napier University)
Published
2025-09-12
Primary Topic
Text Readability and Simplification
Type
article
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article

NapierNLP at CheckThat! 2025: Detecting Subjectivity with LLMs and Model Fusion Notebook for the CheckThat! Lab at CLEF 2025

Edinburgh Napier Research Repository (Edinburgh Napier University)
Text Readability and Simplification
article

NapierNLP at CheckThat! 2025: Detecting Subjectivity with LLMs and Model Fusion Notebook for the CheckThat! Lab at CLEF 2025

article en

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.

Edinburgh Napier Research Repository (Edinburgh Napier University)
Quality Education
Openalex Percentile: Top 20%
Text Readability and Simplification
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