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Zach Brannelly

3 papers here

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author2
  • last author1

Across the 3 of 3 papers where every author was matched, so the position is known.

fields
  • cs.SE2
  • cs.CL1

identity via Semantic Scholar / OpenAlex

most citedSeven Failure Points When Engineering a Retrieval Augmented Generation System

9 citations · 15 across the 3 of their papers we have counts for

collaborators

3 papers

cs.CL2024★ 6 cited

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…

cs.SE2024

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…

cs.SE2024★ 9 cited

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…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.