LLMS factors for unrelated information when recommending treatment | MIT News
Research by MIT researchers found that nonclinical information in patient messages, such as typos, extra white space, missing gender markers, or uncertain, dramatic and informal language use, can deploy large-scale language models (LLMs) deployed to make treatment recommendations. They found that by making stylistic or grammatical changes to their messages, even if the LLM should…
