155 citations · 251 across the 23 of their papers we have counts for
13 papers · 1 filter
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…
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
However, building reliable data agents remains difficult because real enterprise data is fragmented across many heterogeneous database systems, with duplicated and inconsistent dat…
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.,…
Featurized-Decomposition Join: Low-Cost Semantic Joins with Guarantees
Sepanta Zeighami, Shreya Shankar, Aditya Parameswaran
Large Language Models (LLMs) are being increasingly used within data systems to process large datasets with text fields. A broad class of such tasks involves a semantic join-joinin…
Multi-Objective Agentic Rewrites for Unstructured Data Processing
Lindsey Linxi Wei, Shreya Shankar, Sepanta Zeighami +3
One year ago, we open-sourced DocETL, a declarative system for LLM-powered data processing that, as of March 2026, has 3.7K GitHub stars and users across domains (e.g., journalism,…
Cut Costs, Not Accuracy: LLM-Powered Data Processing with Guarantees
Sepanta Zeighami, Shreya Shankar, Aditya Parameswaran
Large Language Models (LLMs) are being increasingly used as a building block in data systems to process large text datasets. To do so, LLM model providers offer multiple LLMs with…
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,…
DocETL: Agentic Query Rewriting and Evaluation for Complex Document Processing
Shreya Shankar, Tristan Chambers, Tarak Shah +2
Analyzing unstructured data has been a persistent challenge in data processing. Large Language Models (LLMs) have shown promise in this regard, leading to recent proposals for decl…
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…
Revisiting Prompt Engineering via Declarative Crowdsourcing
Aditya G. Parameswaran, Shreya Shankar, Parth Asawa +2
Large language models (LLMs) are incredibly powerful at comprehending and generating data in the form of text, but are brittle and error-prone. There has been an advent of toolkits…
Moving Fast With Broken Data
Shreya Shankar, Labib Fawaz, Karl Gyllstrom +1
Machine learning (ML) models in production pipelines are frequently retrained on the latest partitions of large, continually-growing datasets. Due to engineering bugs, partitions i…