9 papers
AERO: Adaptive Ensemble-Disagreement Routing for Oracle Feedback in Sample-Efficient Online RLHF
Shrey Singhal, Nannan Huang, Haytham M. Fayek
Aligning Large Language Models (LLMs) with human preferences via Reinforcement Learning from Human Feedback (RLHF) has driven substantial improvements across a wide range of tasks,…
When to Trust: A Causality-Aware Calibration Framework for Accurate Knowledge Graph Retrieval-Augmented Generation
Jing Ren, Bowen Li, Ziqi Xu +3
Knowledge Graph Retrieval-Augmented Generation (KG-RAG) extends the RAG paradigm by incorporating structured knowledge from knowledge graphs, enabling Large Language Models (LLMs)…
Fine-Grained Traceability for Transparent ML Pipelines
Liping Chen, Mujie Liu, Haytham Fayek
Modern machine learning systems are increasingly realised as multistage pipelines, yet existing transparency mechanisms typically operate at a model level: they describe what a sys…
When to Invoke: Refining LLM Fairness with Toxicity Assessment
Jing Ren, Bowen Li, Ziqi Xu +6
Large Language Models (LLMs) are increasingly used for toxicity assessment in online moderation systems, where fairness across demographic groups is essential for equitable treatme…
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…
LiteFat: Lightweight Spatio-Temporal Graph Learning for Real-Time Driver Fatigue Detection
Jing Ren, Suyu Ma, Hong Jia +5
Detecting driver fatigue is critical for road safety, as drowsy driving remains a leading cause of traffic accidents. Many existing solutions rely on computationally demanding deep…
Foundation Models for Anomaly Detection: Vision and Challenges
Jing Ren, Tao Tang, Hong Jia +6
As data continues to grow in volume and complexity across domains such as finance, manufacturing, and healthcare, effective anomaly detection is essential for identifying irregular…
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…