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This research investigates whether large language models can instantiate the computational structures of psychopathology, finding that these network-theoretic patterns emerge and become denser as model size increases. The study argues against mere pattern matching or persona modeling, suggesting a genuine emergence of pathological functions within the architecture itself. This work is critical for understanding the internal dynamics and potential risks of scaling AI systems beyond standard capability benchmarks.
Emergence of psychopathological computations in large language models
arXiv:2504.08016v3 Announce Type: replace-cross Abstract: Can large language models (LLMs) instantiate computations of psychopathology? In this work, we establish a computational-theoretical framework to provide an account of psychopathology applicable to LLMs. Based on the framework, we conduct experiments supporting two key claims: first, that network-theoretic computational structures of psychopathology exist in LLMs; and second, that executing these computational structures results in psychopathological functions. We further observe that as LLM size increases, the computational structure of psychopathology becomes denser and the functions more effective. Taken together, the results suggest that network-theoretic computations of psychopathology may have emerged in LLMs. We discuss alternative explanations, including pattern matching, persona modeling, and semantic coherence, and argue that they are either complementary to our interpretation or less consistent with the data.
Abstract:Can large language models (LLMs) instantiate computations of psychopathology? In this work, we establish a computational-theoretical framework to provide an account of psychopathology applicable to LLMs. Based on the framework, we conduct experiments supporting two key claims: first, that network-theoretic computational structures of psychopathology exist in LLMs; and second, that executing these computational structures results in psychopathological functions. We further observe that as LLM size increases, the computational structure of psychopathology becomes denser and the functions more effective. Taken together, the results suggest that network-theoretic computations of psychopathology may have emerged in LLMs. We discuss alternative explanations, including pattern matching, persona modeling, and semantic coherence, and argue that they are either complementary to our interpretation or less consistent with the data.
Neurons and Cognition (q-bio.NC); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
arXiv:2504.08016 [q-bio.NC]
(or arXiv:2504.08016v3 [q-bio.NC] for this version)
https://doi.org/10.48550/arXiv.2504.08016 Focus to learn more arXiv-issued DOI via DataCite
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