4 citations · 5 across the 6 of their papers we have counts for
6 papers
Relative Parameter Importance in Task-Agnostic Replay-Free Continual Learning
Malavika Suresh, Ikechukwu Nkisi-Orji, Nirmalie Wiratunga
Achieving continual learning (CL) with deep neural networks requires balancing stability and plasticity while enabling knowledge transfer. In this work, we focus on offline learnin…
RAPT: Retrieval-Augmented Post-hoc Thresholding for Multi-Label Classification
Lasal Jayawardena, Nirmalie Wiratunga, Ikechukwu Nkisi-Orji +1
Industrial multi-label document understanding pipelines score candidate labels and threshold or rank them to form a label set per document. This early selection step directly affec…
Cross-Layer Attention Probing for Fine-Grained Hallucination Detection
Malavika Suresh, Rahaf Aljundi, Ikechukwu Nkisi-Orji +1
With the large-scale adoption of Large Language Models (LLMs) in various applications, there is a growing reliability concern due to their tendency to generate inaccurate text, i.e…
iSee: Advancing Multi-Shot Explainable AI Using Case-based Recommendations
Anjana Wijekoon, Nirmalie Wiratunga, David Corsar +7
Explainable AI (XAI) can greatly enhance user trust and satisfaction in AI-assisted decision-making processes. Recent findings suggest that a single explainer may not meet the dive…
CBR-RAG: Case-Based Reasoning for Retrieval Augmented Generation in LLMs for Legal Question Answering
Nirmalie Wiratunga, Ramitha Abeyratne, Lasal Jayawardena +6
Retrieval-Augmented Generation (RAG) enhances Large Language Model (LLM) output by providing prior knowledge as context to input. This is beneficial for knowledge-intensive and exp…
DisCERN:Discovering Counterfactual Explanations using Relevance Features from Neighbourhoods
Nirmalie Wiratunga, Anjana Wijekoon, Ikechukwu Nkisi-Orji +3
Counterfactual explanations focus on "actionable knowledge" to help end-users understand how a machine learning outcome could be changed to a more desirable outcome. For this purpo…