LLM4DICE

Large Language Models (LLMs) are a hot and rapidly evolving research topic. In particular, their recent successes in summarization, question-answering, and code generation with AI pair programming make them attractive candidates in the field of error verification. We propose to harness these LLMs capabilities with fine-tuning on carefully generated datasets through a novel clustering strategy based on Natural Language Processing (NLP) techniques and code embedding to assist bug detection and correction, targeting hard domains such as parallel program verification.