Jun-Wei Zhang

Strong yet Controllable LLM Abstention without Learning to Abstain

Large language models (LLMs) can reduce hallucination by abstaining from questions they would likely get wrong. Abstention can be implemented outside the model (selective answering) using a confidence threshold that controls the answer rate (coverage), or learned inside it (abstention training), which can perform bette...

Bug Whisperer: Unleashing LLMs to Craft Context-Aware Tests Guided by Path Constraints and Bug Clues

On the Evaluation of Large Language Models in Unit Test Evolution (Experience Paper)

Large language models (LLMs) have recently shown promising potential in automating unit test evolution for evolving software systems. However, the effectiveness of LLMs in unit test evolution remains insufficiently understood, particularly with respect to prompt design choices, in-context learning (ICL) strategies, and...