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This content will become publicly available on December 10, 2025

Title: The ALCHEmist: Automated Labeling 500x CHEaper Than LLM Data Annotators
Large pretrained models can be used as annotators, helping replace or augment crowdworkers and enabling distilling generalist models into smaller specialist models. Unfortunately, this comes at a cost: employing top-of-the-line models often requires paying thousands of dollars for API calls, while the resulting datasets are static and challenging to audit. To address these challenges, we propose a simple alternative: rather than directly querying labels from pretrained models, we task models to generate programs that can produce labels. These programs can be stored and applied locally, re-used and extended, and cost orders of magnitude less. Our system, Alchemist, obtains comparable to or better performance than large language model-based annotation in a range of tasks for a fraction of the cost: on average, improvements amount to a 12.9% enhancement while the total labeling costs across all datasets are reduced by a factor of approximately 500×. We release our code here: https://github.com/SprocketLab/Alchemist  more » « less
Award ID(s):
2106707
PAR ID:
10595224
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
NeurIPS
Date Published:
Format(s):
Medium: X
Location:
Conference on Neural Information Processing Systems (NeurIPS)
Sponsoring Org:
National Science Foundation
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