3 papers
cs.SE2026
CodeChat-Eval: Evaluating Large Language Models in Multi-Turn Code Refinement Dialogues
Guoxiang Guo, Kla Tantithamthavorn, Neelofar Neelofar +2
Large Language Models (LLMs) are increasingly used in software engineering to generate and refine code. In practice, developers often continue from an initial code generation reque…
cs.LG2026
Next Generation Active Learning: Mixture of LLMs in the Loop
Yuanyuan Qi, Xiaohao Yang, Jueqing Lu +4
With the rapid advancement and strong generalization capabilities of large language models (LLMs), they have been increasingly incorporated into the active learning pipelines as an…
cs.SE2024
MORTAR: Multi-turn Metamorphic Testing for LLM-based Dialogue Systems
Aaron Guoxiang Guo, Aldeida Aleti, Neelofar Neelofar +3
With the widespread application of LLM-based dialogue systems in daily life, quality assurance has become more important than ever. Recent research has successfully introduced meth…