7 citations · 14 across the 4 of their papers we have counts for
4 papers · 1 filter
LLMLOOP: Improving LLM-Generated Code and Tests through Automated Iterative Feedback Loops
Ravin Ravi, Dylan Bradshaw, Stefano Ruberto +2
Large Language Models (LLMs) are showing remarkable performance in generating source code, yet the generated code often has issues like compilation errors or incorrect code. Resear…
LLMORPH: Automated Metamorphic Testing of Large Language Models
Steven Cho, Stefano Ruberto, Valerio Terragni
Automated testing is essential for evaluating and improving the reliability of Large Language Models (LLMs), yet the lack of automated oracles for verifying output correctness rema…
Metamorphic Testing of Large Language Models for Natural Language Processing
Steven Cho, Stefano Ruberto, Valerio Terragni
Using large language models (LLMs) to perform natural language processing (NLP) tasks has become increasingly pervasive in recent times. The versatile nature of LLMs makes them app…
Automated Trustworthiness Oracle Generation for Machine Learning Text Classifiers
Lam Nguyen Tung, Steven Cho, Xiaoning Du +4
Machine learning (ML) for text classification has been widely used in various domains. These applications can significantly impact ethics, economics, and human behavior, raising se…