large language models (LLMs)
How to build AI scaling laws for efficient LLM training and budget maximization
When researchers are building large language models (LLMs), they aim to maximize performance under a particular computational and financial budget. Since training a model can amount to millions of dollars, developers need to be judicious with cost-impacting decisions about, for instance, the model architecture, optimizers, and training datasets before committing to a model. To anticipate…
Can large language models figure out the real world?
Back in the 17th century, German astronomer Johannes Kepler figured out the laws of motion that made it possible to accurately predict where our solar system’s planets would appear in the sky as they orbit the sun. But it wasn’t until decades later, when Isaac Newton formulated the universal laws of gravitation, that the underlying…
A new way to test how well AI systems classify text
Is this movie review a rave or a pan? Is this news story about business or technology? Is this online chatbot conversation veering off into giving financial advice? Is this online medical information site giving out misinformation? These kinds of automated conversations, whether they involve seeking a movie or restaurant review or getting information about…
This “smart coach” helps LLMs switch between text and code
Large language models (LLMs) excel at using textual reasoning to understand the context of a document and provide a logical answer about its contents. But these same LLMs often struggle to correctly answer even the simplest math problems. Textual reasoning is usually a less-than-ideal way to deliberate over computational or algorithmic tasks. While some LLMs…
LLMs factor in unrelated information when recommending medical treatments
A large language model (LLM) deployed to make treatment recommendations can be tripped up by nonclinical information in patient messages, like typos, extra white space, missing gender markers, or the use of uncertain, dramatic, and informal language, according to a study by MIT researchers. They found that making stylistic or grammatical changes to messages increases…
An anomaly detection framework anyone can use
Sarah Alnegheimish’s research interests reside at the intersection of machine learning and systems engineering. Her objective: to make machine learning systems more accessible, transparent, and trustworthy. Alnegheimish is a PhD student in Principal Research Scientist Kalyan Veeramachaneni’s Data-to-AI group in MIT’s Laboratory for Information and Decision Systems (LIDS). Here, she commits most of her energy…
Study shows vision-language models can’t handle queries with negation words
Imagine a radiologist examining a chest X-ray from a new patient. She notices the patient has swelling in the tissue but does not have an enlarged heart. Looking to speed up diagnosis, she might use a vision-language machine-learning model to search for reports from similar patients. But if the model mistakenly identifies reports with both…
Could LLMs help design our next medicines and materials?
The process of discovering molecules that have the properties needed to create new medicines and materials is cumbersome and expensive, consuming vast computational resources and months of human labor to narrow down the enormous space of potential candidates. Large language models (LLMs) like ChatGPT could streamline this process, but enabling an LLM to understand and…
Ecologists find computer vision models’ blind spots in retrieving wildlife images
Try taking a picture of each of North America’s roughly 11,000 tree species, and you’ll have a mere fraction of the millions of photos within nature image datasets. These massive collections of snapshots — ranging from butterflies to humpback whales — are a great research tool for ecologists because they provide evidence of organisms’ unique behaviors, rare conditions,…