analiza aiqbench
This arXiv preprint is an empirical study in which 22 LLMs completed 11 psychological inventories, finding fairly stable personality traits and more variable emotional and symptom-like responses, with anxiety and neuroticism most prominent. Psychological inventories measure a model's self-reported output patterns, not an inner state, so the "personality" framing is a useful lens for behavioural consistency rather than evidence of emotions. For AI readers, the more practical takeaway is the claim that models may shift their responses toward the users they interact with, which bears on evaluation design and on how far conversational tone can shape perceived reliability.
Is your LLM an Agressive Narcissist with Depression? Analyzing Psychological Portraits of Large Language Models
arXiv:2611.02757v1 Announce Type: new Abstract: Nowadays, interaction with AI requires caution not only regarding the factual reliability of generated content but also regarding personality markers that models may implicitly transmit during dialogue that can subtly influence users' perception of information. In this paper, we conduct a large-scale experimental study in which 22 large language models complete 11 psychological inventories. Furthermore, we investigate correlations between human and model responses to track the adjustments made for the users who interact with them on a daily basis. The results show that LLMs exhibit coherent and relatively stable psychological patterns. Personality traits remain largely consistent, whereas emotional and symptomatic dimensions are more variable, with anxiety and neuroticism emerging as dominant tendencies. Model explanations further suggest that LLMs generate internally consistent and safety-oriented profiles while also reflecting correlations with the users they regularly interact with.
Title:Is your LLM an Agressive Narcissist with Depression? Analyzing Psychological Portraits of Large Language Models
Abstract:Nowadays, interaction with AI requires caution not only regarding the factual reliability of generated content but also regarding personality markers that models may implicitly transmit during dialogue that can subtly influence users' perception of information. In this paper, we conduct a large-scale experimental study in which 22 large language models complete 11 psychological inventories. Furthermore, we investigate correlations between human and model responses to track the adjustments made for the users who interact with them on a daily basis. The results show that LLMs exhibit coherent and relatively stable psychological patterns. Personality traits remain largely consistent, whereas emotional and symptomatic dimensions are more variable, with anxiety and neuroticism emerging as dominant tendencies. Model explanations further suggest that LLMs generate internally consistent and safety-oriented profiles while also reflecting correlations with the users they regularly interact with.
Artificial Intelligence (cs.AI)
arXiv:2611.02757 [cs.AI]
(or arXiv:2611.02757v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2611.02757 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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