AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks
Abstract
The fields of both Natural Language Processing (NLP) and Automated Machine Learning (AutoML) have achieved remarkable results over the past years. In NLP, especially Large Language Models (LLMs) have experienced a rapid series of breakthroughs very recently. We envision that the two fields can radically push the boundaries of each other through tight integration. To showcase this vision, we explore the potential of a symbiotic relationship between AutoML and LLMs, shedding light on how they can benefit each other. In particular, we investigate both the opportunities to enhance AutoML approaches with LLMs from different perspectives and the challenges of leveraging AutoML to further improve LLMs. To this end, we survey existing work, and we critically assess risks. We strongly believe that the integration of the two fields has the potential to disrupt both fields, NLP and AutoML. By highlighting conceivable synergies, but also risks, we aim to foster further exploration at the intersection of AutoML and LLMs.
Keywords
Cite
@article{arxiv.2306.08107,
title = {AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks},
author = {Alexander Tornede and Difan Deng and Theresa Eimer and Joseph Giovanelli and Aditya Mohan and Tim Ruhkopf and Sarah Segel and Daphne Theodorakopoulos and Tanja Tornede and Henning Wachsmuth and Marius Lindauer},
journal= {arXiv preprint arXiv:2306.08107},
year = {2024}
}
Comments
Submitted and accepted at TMLR: https://openreview.net/forum?id=cAthubStyG