153 citations · 186 across the 10 of their papers we have counts for
10 papers
CodeR: Issue Resolving with Multi-Agent and Task Graphs
Dong Chen, Shaoxin Lin, Muhan Zeng +14
GitHub issue resolving recently has attracted significant attention from academia and industry. SWE-bench is proposed to measure the performance in resolving issues. In this paper,…
Evaluating and Mitigating Linguistic Discrimination in Large Language Models
Guoliang Dong, Haoyu Wang, Jun Sun +1
By training on text in various languages, large language models (LLMs) typically possess multilingual support and demonstrate remarkable capabilities in solving tasks described in…
Experimenting a New Programming Practice with LLMs
Simiao Zhang, Jiaping Wang, Guoliang Dong +3
The recent development on large language models makes automatically constructing small programs possible. It thus has the potential to free software engineers from low-level coding…
PTE: Axiomatic Semantics based Compiler Testing
Guoliang Dong, Jun Sun, Richard Schumi +2
The correctness of a compiler affects the correctness of every program written in the language, and thus must be thoroughly evaluated. Existing automatic compiler testing methods h…
Prompting Frameworks for Large Language Models: A Survey
Xiaoxia Liu, Jingyi Wang, Jun Sun +5
Since the launch of ChatGPT, a powerful AI Chatbot developed by OpenAI, large language models (LLMs) have made significant advancements in both academia and industry, bringing abou…
Repairing Adversarial Texts through Perturbation
Guoliang Dong, Jingyi Wang, Jun Sun +5
It is known that neural networks are subject to attacks through adversarial perturbations, i.e., inputs which are maliciously crafted through perturbations to induce wrong predicti…
Automatic Fairness Testing of Neural Classifiers through Adversarial Sampling
Peixin Zhang, Jingyi Wang, Jun Sun +5
Although deep learning has demonstrated astonishing performance in many applications, there are still concerns about its dependability. One desirable property of deep learning appl…
Towards Repairing Neural Networks Correctly
Guoliang Dong, Jun Sun, Jingyi Wang +2
Neural networks are increasingly applied to support decision making in safety-critical applications (like autonomous cars, unmanned aerial vehicles and face recognition based authe…
Towards Interpreting Recurrent Neural Networks through Probabilistic Abstraction
Guoliang Dong, Jingyi Wang, Jun Sun +5
Neural networks are becoming a popular tool for solving many real-world problems such as object recognition and machine translation, thanks to its exceptional performance as an end…
Adversarial Sample Detection for Deep Neural Network through Model Mutation Testing
Jingyi Wang, Guoliang Dong, Jun Sun +2
Deep neural networks (DNN) have been shown to be useful in a wide range of applications. However, they are also known to be vulnerable to adversarial samples. By transforming a nor…