9 citations · 26 across the 5 of their papers we have counts for
6 papers
TaskEval: Synthesised Evaluation for Foundation-Model Tasks
Dilani Widanapathiranage, Scott Barnett, Stefanus Kurniawan +1
Hallucinations are a key concern when creating applications that rely on Foundation models (FMs). Understanding where and how these subtle failures occur in an application relies o…
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…
Fine-Tuning or Fine-Failing? Debunking Performance Myths in Large Language Models
Scott Barnett, Zac Brannelly, Stefanus Kurniawan +1
Large Language Models (LLMs) have the unique capability to understand and generate human-like text from input queries. When fine-tuned, these models show enhanced performance on do…
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…
Seven Failure Points When Engineering a Retrieval Augmented Generation System
Scott Barnett, Stefanus Kurniawan, Srikanth Thudumu +2
Software engineers are increasingly adding semantic search capabilities to applications using a strategy known as Retrieval Augmented Generation (RAG). A RAG system involves findin…
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…