23 citations · 64 across the 18 of their papers we have counts for
12 papers · 1 filter
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…
The Cambridge Report on Database Research
Anastasia Ailamaki, Samuel Madden, Daniel Abadi +43
On October 19 and 20, 2023, the authors of this report convened in Cambridge, MA, to discuss the state of the database research field, its recent accomplishments and ongoing challe…
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…
Efficient and Compact Spreadsheet Formula Graphs
Dixin Tang, Fanchao Chen, Christopher De Leon +4
Spreadsheets are one of the most popular data analysis tools, wherein users can express computation as formulae alongside data. The ensuing dependencies are tracked as formula grap…