AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks

verfasst von

Alexander Tornede, Difan Deng, Theresa Eimer, Joseph Giovanelli, Aditya Mohan, Tim Ruhkopf, Sarah Segel, Daphne Theodorakopoulos, Tanja Tornede, Henning Wachsmuth, Marius Lindauer

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.

Organisationseinheit(en)

Fachgebiet Maschinelles Lernen
Institut für Künstliche Intelligenz
Fachgebiet Maschinelle Sprachverarbeitung

Externe Organisation(en)

Deutsches Forschungszentrum for Künstliche Intelligenz GmbH (DFKI)

Typ

Preprint

Publikationsdatum

13.06.2023

Publikationsstatus

Elektronisch veröffentlicht (E-Pub)

Elektronische Version(en)

https://doi.org/10.48550/arXiv.2306.08107 (Zugang: Offen)