1 citations · 1 across the 6 of their papers we have counts for
6 papers
MUSE: An Interactive Meta-Agent for Understanding and Steering LLM-powered Data Science Systems
Wei-Hao Chen, Weixi Tong, Yuan Tian +2
Recent advances in large language models have enabled a new class of agentic data science systems that allow users to complete complex data science workflows through natural langua…
FLARE: Fine-Grained Diagnostic Feedback for LLM Code Refinement
Yinsheng Yao, Hongxiang Zhang, Weixi Tong +1
Large language models often generate code with bugs. Existing methods rely on feedback signals such as test failures and self-critiques to iteratively refine the generated code. Su…
Training Computer Use Agents to Assess the Usability of Graphical User Interfaces
Alice Gao, Weixi Tong, Rishab Vempati +4
Usability testing with experts and potential users can assess the effectiveness, efficiency, and user satisfaction of graphical user interfaces (GUIs) but doing so remains a costly…
Mango: Multi-Agent Web Navigation via Global-View Optimization
Weixi Tong, Yifeng Di, Tianyi Zhang
Existing web agents typically initiate exploration from the root URL, which is inefficient for complex websites with deep hierarchical structures. Without a global view of the webs…
Dango: A Mixed-Initiative Data Wrangling System using Large Language Model
Wei-Hao Chen, Weixi Tong, Amanda Case +1
Data wrangling is a time-consuming and challenging task in a data science pipeline. While many tools have been proposed to automate or facilitate data wrangling, they often misinte…
CodeJudge: Evaluating Code Generation with Large Language Models
Weixi Tong, Tianyi Zhang
Large Language Models (LLMs) have shown promising performance in code generation. However, how to reliably evaluate code generated by LLMs remains an unresolved problem. This paper…