2 citations · 3 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…
Explore-on-Graph: Incentivizing Autonomous Exploration of Large Language Models on Knowledge Graphs with Path-refined Reward Modeling
Shiqi Yan, Yubo Chen, Ruiqi Zhou +8
The reasoning process of Large Language Models (LLMs) is often plagued by hallucinations and missing facts in question-answering tasks. A promising solution is to ground LLMs' answ…
Enhancing Code Generation via Bidirectional Comment-Level Mutual Grounding
Yifeng Di, Tianyi Zhang
Large Language Models (LLMs) have demonstrated unprecedented capability in code generation. However, LLM-generated code is still plagued with a wide range of functional errors, esp…
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…
Towards Understanding the Characteristics of Code Generation Errors Made by Large Language Models
Zhijie Wang, Zijie Zhou, Da Song +4
Large Language Models (LLMs) have demonstrated unprecedented capabilities in code generation. However, there remains a limited understanding of code generation errors that LLMs can…