8 citations · 16 across the 10 of their papers we have counts for
5 papers · 1 filter
Jupiter: Enhancing LLM Data Analysis Capabilities via Notebook and Inference-Time Value-Guided Search
Shuocheng Li, Yihao Liu, Silin Du +7
Large language models (LLMs) have shown great promise in automating data science workflows, but existing models still struggle with multi-step reasoning and tool use, which limits…
MMTU: A Massive Multi-Task Table Understanding and Reasoning Benchmark
Junjie Xing, Yeye He, Mengyu Zhou +6
Tables and table-based use cases play a crucial role in many important real-world applications, such as spreadsheets, databases, and computational notebooks, which traditionally re…
Auto-Prep: Holistic Prediction of Data Preparation Steps for Self-Service Business Intelligence
Eugenie Y. Lai, Yeye He, Surajit Chaudhuri
Business Intelligence (BI) plays a critical role in empowering modern enterprises to make informed data-driven decisions, and has grown into a billion-dollar business. Self-service…
Auto-Test: Learning Semantic-Domain Constraints for Unsupervised Error Detection in Tables
Qixu Chen, Yeye He, Raymond Chi-Wing Wong +5
Data cleaning is a long-standing challenge in data management. While powerful logic and statistical algorithms have been developed to detect and repair data errors in tables, exist…
TableLoRA: Low-rank Adaptation on Table Structure Understanding for Large Language Models
Xinyi He, Yihao Liu, Mengyu Zhou +5
Tabular data are crucial in many fields and their understanding by large language models (LLMs) under high parameter efficiency paradigm is important. However, directly applying pa…