Large language models may help psychiatrists resolve unanswered questions about mental illness.

What’s new: Researchers from University College London, Beijing Normal University, and Lisbon’s Champalimaud Centre for the Unknown used a large language model to measure differences in the ways people with schizophrenia use words.

Key insight: Neuroscientists theorize that schizophrenia disturbs the brain’s ability to represent concepts. When given a task like “name as many animals as you can in five minutes,” patients with schizophrenia would propose names in a less-predictable order than people who haven’t. In general, the consecutive names produced by people with schizophrenia would be less semantically related than those produced by others.

How it works: The authors asked 26 people who had been diagnosed with schizophrenia and 26 people who hadn’t to name as many animals as they could in five minutes. They also asked the subjects to name as many words that start with the letter “P” as they could in five minutes.

  • The authors analyzed the randomness of the lists by comparing them to an “optimal” order based on embeddings generated by a fastText model that was pretrained on text from the web. Given a word, fastText embedded it. They computed the cosine similarity — a measure of semantic relationship — between every pair of words in each list. 
  • They used the traveling salesman algorithm to compute an optimal order of words in each list, starting with the first word. The optimal order contained all words, and it maximized the similarity between consecutive words. 
  • To measure the randomness of the orders produced by people in the experiment, first they totaled the cosine similarities between consecutive words in each list for original and optimal orders. Then they found the difference in total cosine similarity between the original and optimal orders.

Results: Responses by subjects with schizophrenia had greater randomness. To control for variations in the contents of various patients’ lists, the researchers expressed the degree of randomness as a standard score, where 0 indicates complete randomness, and the lower the negative number, the more optimal the order. On average, people with schizophrenia achieved -5.81, while people without schizophrenia achieved -7.02.

Why it matters: The fastText model’s embeddings helped the authors demonstrate a relationship between cognitive activity and psychiatric symptoms that previously was purely theoretical. Such a relationship has been difficult to establish through brain imaging or traditional testing. 

We’re thinking: It’s important to note that the authors don’t propose using their method as a diagnostic tool to determine whether or not a patient has schizophrenia. Unlike diagnosing, say, a cancerous tumor, establishing ground truth in mental illness is extremely complicated. The fact that AI-based measurements agree with doctors’ assessments is a very positive sign.


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