Category: AI

The Challenge of Fine-Tuning Large Transformer Models

Self-attention enables transformer models to capture long-range dependencies in text, which is crucial for comprehending complex language patterns. These models work…

MIT Morningside Academy for Design (MAD) Fellow Caitlin Morris is an architect, artist, researcher, and educator who has studied psychology and used online learning tools to teach herself coding and other skills….

During his first year at MIT in 2021, Matthew Caren ’25 received an intriguing email inviting students to apply to become members of the MIT Schwarzman College of Computing’s (SCC) Undergraduate Advisory Group (UAG)….

Research has shown that large language models (LLMs) tend to overemphasize information at the beginning and end of a document or conversation, while neglecting the middle.

This “position bias” means that, if a lawyer…

Python A2A is an implementation of Google’s Agent-to-Agent (A2A) protocol, which enables AI agents to communicate with each other using a shared, standardized format—eliminating the need for custom integration between…

The Challenge of Updating LLM Knowledge

LLMs have shown outstanding performance for various tasks through extensive pre-training on vast datasets. However, these models frequently generate outdated or inaccurate information and…

On May 6, MIT AgeLab’s Advanced Vehicle Technology (AVT) Consortium, part of the MIT Center for Transportation and Logistics, celebrated 10 years of its global academic-industry collaboration. AVT was founded with the…

The Need for Efficient On-Device Language Models

Large language models have become integral to AI systems, enabling tasks like multilingual translation, virtual assistance, and automated reasoning through transformer-based…