LLMs factor in unrelated information when recommending medical treatments

An MIT study finds non-clinical information in patient messages, like typos, extra whitespace, or colorful language, can reduce the accuracy of a large language model deployed to make treatment recommendations. The LLMs were consistently less accurate for female patients, even when all gender markers were removed from the text.