3 citations · 17 across the 21 of their papers we have counts for
27 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…
Who's Keeping Score? Interactive Steering of LLM-Powered Scoring with Attune
Bhavya Chopra, Meng Chen, Rebecca Dang +5
Large language models (LLMs) are increasingly used to score text records at scale (e.g., rating candidate resumes on a 1-5 scale). However, existing LLM-powered approaches do not a…
Semantic Data Processing with Holistic Data Understanding
Youran Sun, Sepanta Zeighami, Bhavya Chopra +2
Semantic operators have increasingly become integrated within data systems to enable processing data using Large Language Models (LLMs). Despite significant recent effort in improv…
Can AI Agents Answer Your Data Questions? A Benchmark for Data Agents
Ruiying Ma, Shreya Shankar, Ruiqi Chen +7
Users across enterprises increasingly rely on AI agents to query their data through natural language. However, building reliable data agents remains difficult because real-world da…
Arming Data Agents with Tribal Knowledge
Shubham Agarwal, Asim Biswal, Sepanta Zeighami +3
Natural language to SQL (NL2SQL) translation enables non-expert users to query relational databases through natural language. Recently, NL2SQL agents, powered by the reasoning capa…
Task Cascades for Efficient Unstructured Data Processing
Shreya Shankar, Sepanta Zeighami, Aditya Parameswaran
Modern database systems allow users to query or process unstructured text or document columns using LLM-powered functions. Users can express an operation in natural language (e.g.,…