6 papers
ClassEval-Pro: A Cross-Domain Benchmark for Class-Level Code Generation
Yeheng Chen, Chaoxiang Xie, Yuling Shi +4
LLMs have achieved strong results on both function-level code synthesis and repository-level code modification, yet a capability that falls between these two extremes -- compositio…
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…
Pruning the Unsurprising: Efficient LLM Reasoning via First-Token Surprisal
Wenhao Zeng, Yaoning Wang, Chao Hu +4
Large Reasoning Models (LRMs) have demonstrated remarkable capabilities by scaling up the length of Chain-of-Thought (CoT). However, excessively long reasoning traces pose substant…
LongCodeZip: Compress Long Context for Code Language Models
Yuling Shi, Yichun Qian, Hongyu Zhang +2
Code generation under long contexts is becoming increasingly critical as Large Language Models (LLMs) are required to reason over extensive information in the codebase. While recen…
Transplant Then Regenerate: A New Paradigm for Text Data Augmentation
Guangzhan Wang, Hongyu Zhang, Beijun Shen +1
Data augmentation is a critical technique in deep learning. Traditional methods like Back-translation typically focus on lexical-level rephrasing, which primarily produces variatio…
On the Effectiveness of Large Language Models in Domain-Specific Code Generation
Xiaodong Gu, Meng Chen, Yalan Lin +6
Large language models (LLMs) such as ChatGPT have shown remarkable capabilities in code generation. Despite significant achievements, they rely on enormous training data to acquire…