2 citations · 2 across the 4 of their papers we have counts for
6 papers
Can LLMs Clean Up Your Mess? A Survey of Application-Ready Data Preparation with LLMs
Wei Zhou, Jun Zhou, Haoyu Wang +16
Data preparation aims to denoise raw datasets, uncover cross-dataset relationships, and extract valuable insights from them, which is essential for a wide range of data-centric app…
LLM/Agent-as-Data-Analyst: A Survey
Zirui Tang, Weizheng Wang, Zihang Zhou +16
Large language models (LLMs) and agent techniques have brought a fundamental shift in the functionality and development paradigm of data analysis tasks (a.k.a LLM/Agent-as-Data-Ana…
MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing
Junbo Niu, Zheng Liu, Zhuangcheng Gu +58
We introduce MinerU2.5, a 1.2B-parameter document parsing vision-language model that achieves state-of-the-art recognition accuracy while maintaining exceptional computational effi…
ST-Raptor: LLM-Powered Semi-Structured Table Question Answering
Zirui Tang, Boyu Niu, Xuanhe Zhou +6
Semi-structured tables, widely used in real-world applications (e.g., financial reports, medical records, transactional orders), often involve flexible and complex layouts (e.g., h…
A Survey of LLM DATA
Xuanhe Zhou, Junxuan He, Wei Zhou +14
The integration of large language model (LLM) and data management (DATA) is rapidly redefining both domains. In this survey, we comprehensively review the bidirectional relationshi…
FeatInsight: An Online ML Feature Management System on 4Paradigm Sage-Studio Platform
Xin Tong, Xuanhe Zhou, Bingsheng He +6
Feature management is essential for many online machine learning applications and can often become the performance bottleneck (e.g., taking up to 70% of the overall latency in sale…