5 papers
Memorization Diagnostics for Code LLMs Should be Scale-Aware
Prateek Kumar Rajput, Abdoul Aziz Bonkoungou, Alberick Euraste Djiré +6
The extent to which large language models for code rely on memorization over genuine understanding remains highly debated. While current literature frequently reports widespread me…
Dynamic Stability of LLM-Generated Code
Prateek Rajput, Abdoul Aziz Bonkoungou, Yewei Song +4
Current evaluations of LLMs for code generation emphasize functional correctness, overlooking the fact that functionally correct solutions can differ significantly in algorithmic c…
Reinforcement Learning-Guided Chain-of-Draft for Token-Efficient Code Generation
Xunzhu Tang, Iyiola Emmanuel Olatunji, Tiezhu Sun +2
LLMs demonstrate surface-level fluency in code generation but struggle with structured reasoning tasks requiring correctness and semantic alignment. While Chain-of-Thought (CoT) pr…
Measuring LLM Code Generation Stability via Structural Entropy
Yewei Song, Tiezhu Sun, Xunzhu Tang +3
Assessing the stability of code generation from large language models (LLMs) is essential for judging their reliability in real-world development. We extend prior "structural-entro…
Just-in-Time Detection of Silent Security Patches
Xunzhu Tang, Zhenghan Chen, Kisub Kim +3
Open-source code is pervasive. In this setting, embedded vulnerabilities are spreading to downstream software at an alarming rate. While such vulnerabilities are generally identifi…