29 citations · 101 across the 38 of their papers we have counts for
13 papers · 1 filter
VRPTEST: Evaluating Visual Referring Prompting in Large Multimodal Models
Zongjie Li, Chaozheng Wang, Chaowei Liu +4
With recent advancements in Large Multimodal Models (LMMs) across various domains, a novel prompting method called visual referring prompting has emerged, showing significant poten…
InstructTA: Instruction-Tuned Targeted Attack for Large Vision-Language Models
Xunguang Wang, Zhenlan Ji, Pingchuan Ma +3
Large vision-language models (LVLMs) have demonstrated their incredible capability in visual question answering. However, this rich visual interaction also makes LVLMs vulnerable t…
Benchmarking and Explaining Large Language Model-based Code Generation: A Causality-Centric Approach
Zhenlan Ji, Pingchuan Ma, Zongjie Li +1
While code generation has been widely used in various software development scenarios, the quality of the generated code is not guaranteed. This has been a particular concern in the…
Split and Merge: Aligning Position Biases in LLM-based Evaluators
Zongjie Li, Chaozheng Wang, Pingchuan Ma +4
Large language models (LLMs) have shown promise as automated evaluators for assessing the quality of answers generated by AI systems. However, these LLM-based evaluators exhibit po…
Enabling Runtime Verification of Causal Discovery Algorithms with Automated Conditional Independence Reasoning (Extended Version)
Pingchuan Ma, Zhenlan Ji, Peisen Yao +2
Causal discovery is a powerful technique for identifying causal relationships among variables in data. It has been widely used in various applications in software engineering. Caus…
ASAP: Automated Sequence Planning for Complex Robotic Assembly with Physical Feasibility
Yunsheng Tian, Karl D. D. Willis, Bassel Al Omari +10
The automated assembly of complex products requires a system that can automatically plan a physically feasible sequence of actions for assembling many parts together. In this paper…
How Can Large Language Models Help Humans in Design and Manufacturing?
Liane Makatura, Michael Foshey, Bohan Wang +15
The advancement of Large Language Models (LLMs), including GPT-4, provides exciting new opportunities for generative design. We investigate the application of this tool across the…
Towards Practical Federated Causal Structure Learning
Zhaoyu Wang, Pingchuan Ma, Shuai Wang
Understanding causal relations is vital in scientific discovery. The process of causal structure learning involves identifying causal graphs from observational data to understand s…
Explain Any Concept: Segment Anything Meets Concept-Based Explanation
Ao Sun, Pingchuan Ma, Yuanyuan Yuan +1
EXplainable AI (XAI) is an essential topic to improve human understanding of deep neural networks (DNNs) given their black-box internals. For computer vision tasks, mainstream pixe…
"Oops, Did I Just Say That?" Testing and Repairing Unethical Suggestions of Large Language Models with Suggest-Critique-Reflect Process
Pingchuan Ma, Zongjie Li, Ao Sun +1
As the popularity of large language models (LLMs) soars across various applications, ensuring their alignment with human values has become a paramount concern. In particular, given…
Causality-Aided Trade-off Analysis for Machine Learning Fairness
Zhenlan Ji, Pingchuan Ma, Shuai Wang +1
There has been an increasing interest in enhancing the fairness of machine learning (ML). Despite the growing number of fairness-improving methods, we lack a systematic understandi…
Demonstration of InsightPilot: An LLM-Empowered Automated Data Exploration System
Pingchuan Ma, Rui Ding, Shuai Wang +2
Exploring data is crucial in data analysis, as it helps users understand and interpret the data more effectively. However, performing effective data exploration requires in-depth k…
On Extracting Specialized Code Abilities from Large Language Models: A Feasibility Study
Zongjie Li, Chaozheng Wang, Pingchuan Ma +5
Recent advances in large language models (LLMs) significantly boost their usage in software engineering. However, training a well-performing LLM demands a substantial workforce for…