1 citations · 1 across the 4 of their papers we have counts for
4 papers
Show Me Why It's Correct: Saving 1/3 of Debugging Time in Program Repair with Interactive Runtime Comparison
Ruixin Wang, Zhongkai Zhao, Le Fang +4
Automated Program Repair (APR) holds the promise of alleviating the burden of debugging and fixing software bugs. Despite this, developers still need to manually inspect each patch…
Collu-Bench: A Benchmark for Predicting Language Model Hallucinations in Code
Nan Jiang, Qi Li, Lin Tan +1
Despite their success, large language models (LLMs) face the critical challenge of hallucinations, generating plausible but incorrect content. While much research has focused on ha…
SELP: Generating Safe and Efficient Task Plans for Robot Agents with Large Language Models
Yi Wu, Zikang Xiong, Yiran Hu +5
Despite significant advancements in large language models (LLMs) that enhance robot agents' understanding and execution of natural language (NL) commands, ensuring the agents adher…
Nova: Generative Language Models for Assembly Code with Hierarchical Attention and Contrastive Learning
Nan Jiang, Chengxiao Wang, Kevin Liu +4
Binary code analysis is the foundation of crucial tasks in the security domain; thus building effective binary analysis techniques is more important than ever. Large language model…