6 papers
Auto-Fill: Learning to Predict Missing Values Accurately with Specialist Language Models
Yurong Liu, Yeye He, Haoyu Dong +4
Predicting missing cell values in tabular data is a fundamental problem in data cleaning. While state-of-the-art reasoning models show great promise in predicting missing values in…
Auto-Relate: A Unified Approach to Discovering Reliable Functional Relationships Leveraging Statistical Tests
Ziyan Han, Yeye He, Shuyuan Kang +8
Tables in spreadsheets, computational notebooks, and databases often contain rich inter-column relationships. Yet these relationships are typically implicit and are often lost when…
Table-LLM-Specialist: Language Model Specialists for Tables using Iterative Generator-Validator Fine-tuning
Junjie Xing, Yeye He, Mengyu Zhou +4
Language models such as GPT and Llama have shown remarkable ability on diverse natural language tasks, yet their performance on complex table tasks (e.g., NL-to-Code and data clean…
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…
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…
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…