3 accepted papers
With the widespread use of LLMs, preserving privacy in user prompts has become crucial, as prompts risk exposing private and sensitive data to cloud LLMs. Conventional techniques like homomorphic encryption (HE), secure multi-party computation, and federated learning (FL) are not well-suited to this
Code complexity metrics such as cyclomatic complexity have long been used to assess software quality and maintainability. With the rapid advancement of large language models (LLMs) on code understanding and generation tasks, an important yet underexplored question arises: do these traditional comple…
Data augmentation is a critical technique in deep learning. Traditional methods like Back-translation typically focus on lexical-level rephrasing, which primarily produces variations with the same semantics. While large language models (LLMs) have enhanced text augmentation by their “knowledge emerg