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Found 10 papers, 0 papers with code
Minimising changes to audit when updating decision trees
Interpretable models are important, but what happens when the model is updated on new training data?
Fine-Tuning or Fine-Failing? Debunking Performance Myths in Large Language Models
This study extends this concept to the integration of LLMs within Retrieval-Augmented Generation (RAG) pipelines, which aim to improve accuracy and relevance by leveraging external corpus data for information retrieval.
LLMs for Test Input Generation for Semantic Caches
Our novel approach uses the reasoning capabilities of LLMs to 1) adapt queries to the domain, 2) synthesise subtle variations to queries, and 3) evaluate the synthesised test dataset.
ML-On-Rails: Safeguarding Machine Learning Models in Software Systems A Case Study
In this paper, we introduce ML-On-Rails, a protocol designed to safeguard ML models, establish a well-defined endpoint interface for different ML tasks, and clear communication between ML providers and ML consumers (software engineers).
Evaluating LLMs on Document-Based QA: Exact Answer Selection and Numerical Extraction using Cogtale dataset
In this paper, we specifically focus on this underexplored context and conduct empirical analysis of LLMs (GPT-4 and GPT-3. 5) on question types, including single-choice, yes-no, multiple-choice, and number extraction questions from documents in zero-shot setting.
Green Runner: A tool for efficient model selection from model repositories
Deep learning models have become essential in software engineering, enabling intelligent features like image captioning and document generation.
Comparative analysis of real bugs in open-source Machine Learning projects -- A Registered Report
Objective: Our objective is to investigate whether there is a discrepancy in the distribution of resolution time between ML and non-ML issues and whether certain categories of ML issues require a longer time to resolve based on real issue reports in open-source applied ML projects.
MLSmellHound: A Context-Aware Code Analysis Tool
In our approach we attempt to resolve this problem by exploring the use of context which includes i) purpose of the source code, ii) technical domain, iii) problem domain, iv) team norms, v) operational environment, and vi) development lifecycle stage to provide contextualised error reporting for code analysis.
Beware the evolving 'intelligent' web service! An integration architecture tactic to guard AI-first components
Intelligent services provide the power of AI to developers via simple RESTful API endpoints, abstracting away many complexities of machine learning.
Interpreting Cloud Computer Vision Pain-Points: A Mining Study of Stack Overflow
The objective of this study is to determine the various pain-points developers face when implementing systems that rely on the most mature of these intelligent services, specifically those that provide computer vision.
