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Related Experiment Video

Updated: May 25, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Automated detection of referential features in schizophrenic speech using large language models.

Derya Çokal1, Melike Filizer2, Martin Villalba1

  • 1Department of German Language and Literature I, Linguistics, University of Cologne, Cologne, Germany.

Neuropsychologia
|May 23, 2026
PubMed
Summary

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This summary is machine-generated.

Individuals with schizophrenia and formal thought disorder (FTD) underuse specific noun phrases in speech. Large language models (LLMs) can automatically identify these linguistic patterns, aiding in clinical assessment.

Area of Science:

  • Psycholinguistics
  • Computational Linguistics
  • Clinical Neuroscience

Background:

  • Cross-linguistic studies reveal distinct noun phrase (NP) distributions in the spontaneous speech of individuals with schizophrenia, particularly those with formal thought disorder (FTD).
  • Extracting referential NP features, crucial for understanding meaning organization, traditionally necessitates manual linguistic annotation.

Purpose of the Study:

  • To investigate the feasibility of using state-of-the-art large language models (LLMs) for the automatic extraction of referential NP features in spontaneous speech.
  • To validate LLM-based findings against manual annotations and assess their utility in identifying linguistic markers associated with schizophrenia and FTD.

Main Methods:

  • Application of LLMs to a manually annotated dataset of comic strip descriptions from individuals with schizophrenia (SZ) (with and without FTD) and neurotypical controls (NC).
Keywords:
Large language models (LLM)Natural language processing (NLP)Noun phrasesReferentialitySchizophrenia

Related Experiment Videos

Last Updated: May 25, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

  • Utilized in-context (few-shot) learning with LLMs to enhance feature extraction accuracy.
  • Comparative analysis of LLM-derived NP features with results from manual annotation.

    • Main Results:

    • LLM-based analyses successfully replicated findings from manual annotations, confirming underuse of definite NPs in the schizophrenia with FTD (SZ+FTD) group.
    • Definite NPs, indicative of discourse-based reference, grammatical complexity, and narrative coherence, were significantly underutilized by the SZ+FTD group.
    • LLMs, particularly with few-shot learning, demonstrated effectiveness in automatically extracting these clinically relevant referential features.

    Conclusions:

    • LLMs provide a promising and automated approach for extracting referential NP features in spontaneous speech.
    • This automated assessment using Natural Language Processing (NLP) can identify clinically significant linguistic deviations validated across languages.
    • LLM-driven analysis offers a scalable method for assessing linguistic patterns relevant to schizophrenia and FTD.