5 papers · 1 filter
REFER: Mitigating Bias in Opinion Summarisation via Frequency Framed Prompting
Nannan Huang, Haytham M. Fayek, Xiuzhen Zhang
Individuals express diverse opinions, a fair summary should represent these viewpoints comprehensively. Previous research on fairness in opinion summarisation using large language…
Less Is More? Examining Fairness in Pruned Large Language Models for Summarising Opinions
Nannan Huang, Haytham M. Fayek, Xiuzhen Zhang
Model compression through post-training pruning offers a way to reduce model size and computational requirements without significantly impacting model performance. However, the eff…
X-Troll: eXplainable Detection of State-Sponsored Information Operations Agents
Lin Tian, Xiuzhen Zhang, Maria Myung-Hee Kim +2
State-sponsored trolls, malicious actors who deploy sophisticated linguistic manipulation in coordinated information campaigns, posing threats to online discourse integrity. While…
CMA-R:Causal Mediation Analysis for Explaining Rumour Detection
Lin Tian, Xiuzhen Zhang, Jey Han Lau
We apply causal mediation analysis to explain the decision-making process of neural models for rumour detection on Twitter. Interventions at the input and network level reveal the…
Bias in Opinion Summarisation from Pre-training to Adaptation: A Case Study in Political Bias
Nannan Huang, Haytham Fayek, Xiuzhen Zhang
Opinion summarisation aims to summarise the salient information and opinions presented in documents such as product reviews, discussion forums, and social media texts into short su…