8 papers
BIRD-History: A Benchmark for History-Driven Text-to-SQL with Fine-Grained Knowledge Annotations
Yunfan Zhou, Qiming Shi, Yizhou Yang +2
While recent Large Language Model (LLM)-based text-to-SQL systems achieve impressive performance on standard benchmarks, they struggle when user queries implicitly rely on domain-s…
AdaLens: Interactive Storyline for Monitoring and Steering Long-Running Agentic Data Analysis
Yangtian Liu, Yan Miao, Shuhan Liu +4
Large language models are pushing data science toward increasingly autonomous and agentic workflows, with recent systems already supporting multi-step and long-running analyses. As…
SKILL-KD: Contrastive Skill Distillation for LLM Agents
Qiming Shi, Yibo Dou, Jiawen Zhu +5
Skill-based prompting has become a practical mechanism for improving LLM agents, yet existing methods often treat skills as summaries of the agent's own experience or of successful…
SPADER: Step-wise Peer Advantage with Diversity-Aware Exploration Rewards for Multi-Answer Question Answering
Qiming Shi, Zhaolu Kang, Yunfan Zhou +2
Large language models are increasingly deployed as tool-augmented agents to acquire information beyond parametric knowledge. While recent work has improved long-horizon tool-use re…
Cerebra: Aligning Implicit Knowledge in Interactive SQL Authoring
Yunfan Zhou, Qiming Shi, Zhongsu Luo +5
LLM-driven tools have significantly lowered barriers to writing SQL queries. However, user instructions are often underspecified, assuming the model understands implicit knowledge,…
ViseGPT: Towards Better Alignment of LLM-generated Data Wrangling Scripts and User Prompts
Jiajun Zhu, Xinyu Cheng, Zhongsu Luo +4
Large language models (LLMs) enable the rapid generation of data wrangling scripts based on natural language instructions, but these scripts may not fully adhere to user-specified…
Xavier: Toward Better Coding Assistance in Authoring Tabular Data Wrangling Scripts
Yunfan Zhou, Xiwen Cai, Qiming Shi +5
Data analysts frequently employ code completion tools in writing custom scripts to tackle complex tabular data wrangling tasks. However, existing tools do not sufficiently link the…
RCInvestigator: Towards Better Investigation of Anomaly Root Causes in Cloud Computing Systems
Shuhan Liu, Yunfan Zhou, Lu Ying +9
Finding the root causes of anomalies in cloud computing systems quickly is crucial to ensure availability and efficiency since accurate root causes can guide engineers to take appr…