Deflating Deflationism: A Critical Perspective on Debunking Arguments Against LLM Mentality
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Abstract (arXiv)
Many people feel compelled to interpret, describe, and respond to Large Language Models (LLMs) as if they possess inner mental lives similar to our own. Responses to this phenomenon have varied. Inflationists hold that at least some folk psychological ascriptions to LLMs are warranted. Deflationists argue that all such attributions of mentality to LLMs are misplaced, often cautioning against the risk that anthropomorphic projection may lead to misplaced trust or potentially even confusion about the moral status of LLMs. We advance this debate by assessing two common deflationary arguments against LLM mentality. What we term the 'robustness strategy' aims to undercut one justification for believing that LLMs are minded entities by showing that putatively cognitive and humanlike behaviours are not robust, failing to generalise appropriately. What we term the 'etiological strategy' undercuts attributions of mentality by challenging naive causal explanations of LLM behaviours, offering alternative causal accounts that weaken the case for mental state attributions. While both strategies offer powerful challenges to full-blown inflationism, we find that neither strategy provides a knock-down case against ascriptions of mentality to LLMs simpliciter. With this in mind, we explore a modest form of inflationism that permits ascriptions of mentality to LLMs under certain conditions. Specifically, we argue that folk practice provides a defeasible basis for attributing mental states and capacities to LLMs provided those mental states and capacities can be understood in metaphysically undemanding terms (e.g. knowledge, beliefs and desires), while greater caution is required when attributing metaphysically demanding mental phenomena such as phenomenal consciousness.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 22 of its 76 references that are in the map. First posted on arXiv 16 Jun 2025.
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References · 22
- 2022Language Models (Mostly) Know What They Knowcited by 1,856
- 2021Ethical and social risks of harm from Language Modelscited by 966
- 2024GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Modelscited by 550
- 2023Large Language Models Fail on Trivial Alterations to Theory-of-Mind Taskscited by 292
- 2023Evaluating the Moral Beliefs Encoded in LLMscited by 215
- 2022Talking About Large Language Modelscited by 175
- 2022Language Models as Agent Modelscited by 132
- 2024The Ethics of Advanced AI Assistantscited by 107
- 2023Consciousness in Artificial Intelligence: Insights from the Science of Consciousnesscited by 104
- 2023Could a Large Language Model be Conscious?cited by 73
- 2023From task structures to world models: What do LLMs know?cited by 46
- 2024Why Do Large Language Models (LLMs) Struggle to Count Letters?cited by 38
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- 2024Standards for Belief Representations in LLMscited by 30
- 2025Multi-turn Evaluation of Anthropomorphic Behaviours in Large Language Modelscited by 25
- 2023Towards Evaluating AI Systems for Moral Status Using Self-Reportscited by 22
- 2023Chatbots as social companions: How people perceive consciousness, human likeness, and social health benefits in machinescited by 17
- 2024Simulacra as Conscious Exoticacited by 16
- 2024Can LLMs make trade-offs involving stipulated pain and pleasure states?cited by 16
- 2025Propositional Interpretability in Artificial Intelligencecited by 12
- 2025Recognizing Artificial Minds: A Philosophical Defense of AI Cognitioncited by 6
- 2024On the attribution of confidence to large language modelscited by 5
- 2024LLMs are Not Just Next Token Predictorscited by 3
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Citing papers per year: 2026: 1.
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