April 17, 2024, 4:43 a.m. | S Deepika Sri, Mohammed Aadil S, Sanjjushri Varshini R, Raja CSP Raman, Gopinath Rajagopal, S Taranath Chan

cs.LG updates on arXiv.org arxiv.org

arXiv:2404.10678v1 Announce Type: cross
Abstract: In the contemporary landscape of technological advancements, the automation of manual processes is crucial, compelling the demand for huge datasets to effectively train and test machines. This research paper is dedicated to the exploration and implementation of an automated approach to generate test cases specifically using Large Language Models. The methodology integrates the use of Open AI to enhance the efficiency and effectiveness of test case generation for training and evaluating Large Language Models. This …

abstract api arxiv automated automation cases cs.lg cs.se datasets demand exploration generate implementation landscape llm machines paper processes research research paper rest rest api test train type

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Research Scientist (Computer Science)

@ Nanyang Technological University | NTU Main Campus, Singapore

Intern - Sales Data Management

@ Deliveroo | Dubai, UAE (Main Office)