large language model

MIT-IBM Watson AI Lab researchers developed an expressive architecture that provides better state tracking and sequential reasoning in LLMs over long texts.

Researchers developed a way to make large language models more adaptable to challenging tasks like strategic planning or process optimization.

Researchers at MIT, NYU, and UCLA develop an approach to help evaluate whether large language models like GPT-4 are equitable enough to be clinically viable for mental health support.

Twelve teams of students and postdocs across the MIT community presented innovative startup ideas with potential for real-world impact.

A new way to increase the capabilities of large language models

Study could lead to LLMs that are better at complex reasoning

Study reveals AI chatbots can detect race, but racial bias reduces response empathy

Students pitch transformative ideas in generative AI at MIT Ignite competition