7 citations · 11 across the 6 of their papers we have counts for
6 papers
The M-factor: A Novel Metric for Evaluating Neural Architecture Search in Resource-Constrained Environments
Srikanth Thudumu, Hy Nguyen, Hung Du +6
Neural Architecture Search (NAS) aims to automate the design of deep neural networks. However, existing NAS techniques often focus on maximising accuracy, neglecting model efficien…
RAGProbe: An Automated Approach for Evaluating RAG Applications
Shangeetha Sivasothy, Scott Barnett, Stefanus Kurniawan +2
Retrieval Augmented Generation (RAG) is increasingly being used when building Generative AI applications. Evaluating these applications and RAG pipelines is mostly done manually, v…
Large language models for generating rules, yay or nay?
Shangeetha Sivasothy, Scott Barnett, Rena Logothetis +4
Engineering safety-critical systems such as medical devices and digital health intervention systems is complex, where long-term engagement with subject-matter experts (SMEs) is nee…
LLMs for Test Input Generation for Semantic Caches
Zafaryab Rasool, Scott Barnett, David Willie +4
Large language models (LLMs) enable state-of-the-art semantic capabilities to be added to software systems such as semantic search of unstructured documents and text generation. Ho…
Evaluating LLMs on Document-Based QA: Exact Answer Selection and Numerical Extraction using Cogtale dataset
Zafaryab Rasool, Stefanus Kurniawan, Sherwin Balugo +7
Document-based Question-Answering (QA) tasks are crucial for precise information retrieval. While some existing work focus on evaluating large language models performance on retrie…
Index-based Solutions for Efficient Density Peak Clustering
Zafaryab Rasool, Rui Zhou, Lu Chen +2
Density Peak Clustering (DPC), a popular density-based clustering approach, has received considerable attention from the research community primarily due to its simplicity and fewe…