Programmatic Breakthrough: AI’s Leap From Language to Logic To Solve Complex Problems

Natural language embedded programs have been introduced to enhance the functionality of large language models. By generating Python code to address queries, NLEPs increase accuracy, efficiency, and transparency. This approach allows models to handle diverse tasks more effectively and could also benefit data privacy and smaller models. Credit: SciTechDaily.com

A new technique enables large language models like GPT-4 to more accurately solve numeric or symbolic reasoning tasks by writing a Python program in code that generates the correct answer to a user’s query. Credit: Christine Daniloff, MIT; iStockand elsewhere have proposed a new technique that enables large language models to solve natural language, math and data analysis, and symbolic reasoning tasks by generating programs.

Luo is joined on the paper by co-lead authors Tianhua Zhang, a graduate student at the Chinese University of Hong Kong; and Jiaxin Ge, an undergraduate at Peking University; Yoon Kim, an assistant professor in MIT’s Department of Electrical Engineering and Computer Science and a member of the Computer Science and Artificial Intelligence Laboratory ; senior author James Glass, senior research scientist and head of the Spoken Language Systems Group in CSAIL; and others.

The user can easily investigate the program and fix any errors in the code directly rather than needing to rerun the entire model to troubleshoot.

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This robot could leap higher than the Statue of Liberty — if we ever build it properlyOwen Hughes is a freelance writer and editor specializing in data and digital technologies. Previously a senior editor at ZDNET, Owen has been writing about tech for more than a decade, during which time he has covered everything from AI, cybersecurity and supercomputers to programming languages and public sector IT.
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