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

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arxiv 2306.08107 v3 pith:YEOL2CLP submitted 2023-06-13 cs.LG cs.CL

Alexander Tornede , Difan Deng , Theresa Eimer , Joseph Giovanelli , Aditya Mohan , Tim Ruhkopf , Sarah Segel , Daphne Theodorakopoulos
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Tanja Tornede Henning Wachsmuth Marius Lindauer
This is my paper · ORCID
classification cs.LGcs.CL
keywords automlllmsfieldslanguageriskschallengesfurtherintegration
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Forward citations

Cited by 4 Pith papers

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  2. Bag of Tricks for Multimodal AutoML with Image, Text, and Tabular Data

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    A 22-dataset benchmark compares existing multimodal AutoML tricks, and an automatic ensemble of those tricks achieves the most robust performance.

  3. PINNsAgent: Automated PDE Surrogation with Large Language Models

    cs.CE 2025-01 conditional novelty 5.0 of 10

    An LLM-based multi-agent system that automates PINNs hyperparameter optimization, beating random and Bayesian search on 12 of 14 benchmark PDEs but only matching or beating the PINNacle benchmark on 6 of 14.

  4. Large Language Models in Cybersecurity: Applications, Vulnerabilities, and Defense Techniques

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    A survey that maps LLM applications, vulnerabilities, and defenses across eight cybersecurity domains, but with significant citation and rigor problems.

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