6 citations · 7 across the 4 of their papers we have counts for
4 papers · 1 filter
MELD: Multi-Task Equilibrated Learning Detector for AI-Generated Text
Chenjun Li, Cheng Wan, Johannes C. Paetzold
Large language models are now embedded in everyday writing workflows, making reliable AI-generated text detection important for academic integrity, content moderation, and provenan…
Seeing is Coding: On the Effectiveness of Vision Language Models in Code Understanding
Yuling Shi, Chaoxiang Xie, Zhensu Sun +7
Large Language Models (LLMs) have achieved remarkable success in source code understanding, yet as software systems grow in scale, computational efficiency has become a critical bo…
From Code to Correctness: Closing the Last Mile of Code Generation with Hierarchical Debugging
Yuling Shi, Songsong Wang, Chengcheng Wan +2
While large language models have made significant strides in code generation, the pass rate of the generated code is bottlenecked on subtle errors, often requiring human interventi…
CodeCipher: Learning to Obfuscate Source Code Against LLMs
Yalan Lin, Chengcheng Wan, Yixiong Fang +1
While large code language models have made significant strides in AI-assisted coding tasks, there are growing concerns about privacy challenges. The user code is transparent to the…