LLMs factor in unrelated information when recommending medical treatments

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…

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An anomaly detection framework anyone can use

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…

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Ecologists find computer vision models’ blind spots in retrieving wildlife images

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,…

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