155 citations · 251 across the 23 of their papers we have counts for
9 papers · 1 filter
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…
Supporting Our AI Overlords: Redesigning Data Systems to be Agent-First
Shu Liu, Soujanya Ponnapalli, Shreya Shankar +12
Large Language Model (LLM) agents, acting on their users' behalf to manipulate and analyze data, are likely to become the dominant workload for data systems in the future. When wor…
Rethinking Dataset Discovery with DataScout
Rachel Lin, Bhavya Chopra, Wenjing Lin +3
Dataset Search -- the process of finding appropriate datasets for a given task -- remains a critical yet under-explored challenge in data science workflows. Assessing dataset suita…
Steering Semantic Data Processing With DocWrangler
Shreya Shankar, Bhavya Chopra, Mawil Hasan +5
Unstructured text has long been difficult to automatically analyze at scale. Large language models (LLMs) now offer a way forward by enabling {\em semantic data processing}, where…
PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines
Reya Vir, Shreya Shankar, Harrison Chase +2
Large language models (LLMs) are increasingly deployed in specialized production data processing pipelines across diverse domains -- such as finance, marketing, and e-commerce. How…
RAG Without the Lag: Interactive Debugging for Retrieval-Augmented Generation Pipelines
Quentin Romero Lauro, Shreya Shankar, Sepanta Zeighami +1
Retrieval-augmented generation (RAG) pipelines have become the de-facto approach for building AI assistants with access to external, domain-specific knowledge. Given a user query,…
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,…