SEED: Domain-Specific Data Curation With Large Language Models

• URL: http://arxiv.org/abs/2310.00749v3
• Date: Wed, 24 Apr 2024 09:50:34 GMT
• Title: SEED: Domain-Specific Data Curation With Large Language Models
• Authors: Zui Chen, Lei Cao, Sam Madden, Tim Kraska, Zeyuan Shang, Ju Fan, Nan Tang, Zihui Gu, Chunwei Liu, Michael Cafarella,
• Abstract summary: We present SEED, an LLM-as-compiler approach that automatically generates domain-specific data curation solutions via Large Language Models (LLMs) SEED features an that automatically selects from the four LLM-assisted modules and forms a hybrid execution pipeline that best fits the task at hand.
• Score: 22.54280367957015
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Data curation tasks that prepare data for analytics are critical for turning data into actionable insights. However, due to the diverse requirements of applications in different domains, generic off-the-shelf tools are typically insufficient. As a result, data scientists often have to develop domain-specific solutions tailored to both the dataset and the task, e.g. writing domain-specific code or training machine learning models on a sufficient number of annotated examples. This process is notoriously difficult and time-consuming. We present SEED, an LLM-as-compiler approach that automatically generates domain-specific data curation solutions via Large Language Models (LLMs). Once the user describes a task, input data, and expected output, the SEED compiler produces a hybrid pipeline that combines LLM querying with more cost-effective alternatives, such as vector-based caching, LLM-generated code, and small models trained on LLM-annotated data. SEED features an optimizer that automatically selects from the four LLM-assisted modules and forms a hybrid execution pipeline that best fits the task at hand. To validate this new, revolutionary approach, we conducted experiments on $9$ datasets spanning over $5$ data curation tasks. In comparison to solutions that use the LLM on every data record, SEED achieves state-of-the-art or comparable few-shot performance, while significantly reducing the number of LLM calls.

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