Jan. 6, 2024, 3 p.m. | Reid Southen

IEEE Spectrum spectrum.ieee.org



This is a guest post. The views expressed here are solely those of the authors and do not represent positions of IEEE Spectrum or the IEEE.

The degree to which large language models (LLMs) might “memorize” some of their training inputs has long been a question, raised by scholars including Google DeepMind’s Nicholas Carlini and the first author of this article (Gary Marcus). Recent empirical work has shown that LLMs are in some instances capable of reproducing, or reproducing with …

authors copyright dall-e 3 deepmind generative generative-ai google google deepmind guest post ieee ieee spectrum inputs language language models large language large language models llms midjourney openai plagiarism question scholars spectrum training visual

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Global Data Architect, AVP - State Street Global Advisors

@ State Street | Boston, Massachusetts

Data Engineer

@ NTT DATA | Pune, MH, IN