Large language models (LLMs) are increasingly being used to interpret and generate human language, yet their ability to process clinical language remains underexplored. This study examined whether three open-source LLMs can infer interviewer questions from participant responses in a semi-structured psychiatric interview (NET) conducted with individuals diagnosed with schizophrenia (n = 107) and neurotypical controls (n = 66). Using cosine similarity between LLM-generated questions and original prompts as a proxy for the precision of the inference, we found that responses from individuals with schizophrenia produced significantly lower similarity scores (beta = –0.165, p < .001). Cosine similarity decreased across the nested structure of the interview, with smaller reductions observed in the schizophrenia group. Although all emotions decreased similarity with fear, only sadness showed a significant interaction with diagnosis, suggesting differential processing of emotional discourse. Model type and generation temperature also influenced outcomes, highlighting variability in model performance. Our findings demonstrate that LLMs systematically struggle to reconstruct interviewer intent from responses by individuals with schizophrenia, reflecting known discourse-level disturbances in the disorder.

We present a study of the linguistic output of the German-speaking writer Robert Walser using NLP. We curated a corpus comprising texts written by Walser during periods of sound health, and writings from the year before his hospitalization, and writings from the first year of his stay in a psychiatric clinic, all likely at- tributed to schizophrenia. Within this corpus, we identified and analyzed a total of 20 lin- guistic markers encompassing established met- rics for lexical diversity, semantic similarity, and syntactic complexity. Additionally, we ex- plored lesser-known markers such as lexical innovation, concreteness, and imageability. No- tably, we introduced two additional markers for phonological similarity for the first time within this context. Our findings reveal sig- nificant temporal dynamics in these markers closely associated with Walser’s contempora- neous diagnosis of schizophrenia. Furthermore, we investigated the relationship between these markers, leveraging them for classification of the schizophrenic episode.