21 citations · 31 across the 12 of their papers we have counts for
13 papers
Bolt-on, Verifiable Provenance for LLM-Powered Data Processing
Yiming Lin, Sepanta Zeighami, Aditya G. Parameswaran
Large Language Models (LLMs) are powerful tools for processing data. However, LLMs are also complex black-boxes, returning answers to queries on data, without any indication for wh…
Scout: Scalable Document Extraction via Data Similarity
Yiming Lin, Chiyu Hao, Shreya Shankar +1
Extracting values from large document collections powers data analysis across many domains. Frontier LLMs extract such values accurately, but processing an entire collection with o…
MinerU-Popo: Universal Post-Processing Model for Structured Document Parsing
Bangrui Xu, Ziyang Miao, Xuanhe Zhou +7
VLM-based OCR models have become the de facto choice for document parsing, as they can accurately extract page-level elements (e.g., paragraphs within individual pages) together wi…
Can AI Agents Answer Your Data Questions? A Benchmark for Data Agents
Ruiying Ma, Shreya Shankar, Ruiqi Chen +7
However, building reliable data agents remains difficult because real enterprise data is fragmented across many heterogeneous database systems, with duplicated and inconsistent dat…
LLM-Powered Proactive Data Systems
Sepanta Zeighami, Yiming Lin, Shreya Shankar +1
With the power of LLMs, we now have the ability to query data that was previously impossible to query, including text, images, and video. However, despite this enormous potential,…
Visual Template Inference for Data Extraction from Documents
Yiming Lin, Mawil Hasan, Rohan Kosalge +2
Many templatized documents are programmatically generated from structured data following a visual template. Such documents include invoices, tax documents, financial reports, and p…
Towards Accurate and Efficient Document Analytics with Large Language Models
Yiming Lin, Madelon Hulsebos, Ruiying Ma +4
Unstructured data formats account for over 80% of the data currently stored, and extracting value from such formats remains a considerable challenge. In particular, current approac…
SPADE: Synthesizing Data Quality Assertions for Large Language Model Pipelines
Shreya Shankar, Haotian Li, Parth Asawa +7
Large language models (LLMs) are being increasingly deployed as part of pipelines that repeatedly process or generate data of some sort. However, a common barrier to deployment are…
Auto-BI: Automatically Build BI-Models Leveraging Local Join Prediction and Global Schema Graph
Yiming Lin, Yeye He, Surajit Chaudhuri
Business Intelligence (BI) is crucial in modern enterprises and billion-dollar business. Traditionally, technical experts like database administrators would manually prepare BI-mod…
QUIP: Query-driven Missing Value Imputation
Yiming Lin, Sharad Mehrotra
Missing values widely exist in real-world data sets, and failure to clean the missing data may result in the poor quality of answers to queries. \yiming{Traditionally, missing valu…
LOCATER: Cleaning WiFi Connectivity Datasets for Semantic Localization
Yiming Lin, Daokun Jiang, Roberto Yus +4
This paper explores the data cleaning challenges that arise in using WiFi connectivity data to locate users to semantic indoor locations such as buildings, regions, rooms. WiFi con…
Data Source Selection for Information Integration in Big Data Era
Yiming Lin, Hongzhi Wang, Jianzhong Li +1
In Big data era, information integration often requires abundant data extracted from massive data sources. Due to a large number of data sources, data source selection plays a cruc…
Efficient Entity Resolution on Heterogeneous Records
Yiming Lin, Hongzhi Wang, Jianzhong Li +1
Entity resolution (ER) is the problem of identifying and merging records that refer to the same real-world entity. In many scenarios, raw records are stored under heterogeneous env…