REVIEW 6 cited by
Does ChatGPT Have a Mind?
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
This paper examines the question of whether Large Language Models (LLMs) like ChatGPT possess minds, focusing specifically on whether they have a genuine folk psychology encompassing beliefs, desires, and intentions. We approach this question by investigating two key aspects: internal representations and dispositions to act. First, we survey various philosophical theories of representation, including informational, causal, structural, and teleosemantic accounts, arguing that LLMs satisfy key conditions proposed by each. We draw on recent interpretability research in machine learning to support these claims. Second, we explore whether LLMs exhibit robust dispositions to perform actions, a necessary component of folk psychology. We consider two prominent philosophical traditions, interpretationism and representationalism, to assess LLM action dispositions. While we find evidence suggesting LLMs may satisfy some criteria for having a mind, particularly in game-theoretic environments, we conclude that the data remains inconclusive. Additionally, we reply to several skeptical challenges to LLM folk psychology, including issues of sensory grounding, the "stochastic parrots" argument, and concerns about memorization. Our paper has three main upshots. First, LLMs do have robust internal representations. Second, there is an open question to answer about whether LLMs have robust action dispositions. Third, existing skeptical challenges to LLM representation do not survive philosophical scrutiny.
Forward citations
Cited by 6 Pith papers
-
Artificial Persons
Non-sentient AI systems could in principle satisfy Rawls' two moral powers and thereby count as political persons without needing sentience.
-
Can AI Rely on the Systematicity of Truth? The Challenge of Modelling Normative Domains
Because normative truths are largely asystematic, language models cannot rely on the systematicity of truth to self-complete and self-correct in ethics and politics, leaving final moral judgment to humans.
-
Propositional Interpretability in Artificial Intelligence
Chalmers proposes propositional interpretability, interpreting AI in terms of beliefs, desires, and credences, and sets the challenge of thought logging all such attitudes over time.
-
Chuck, Wilson and the emergence of artificial minds in human-AI conversations
LLM-simulated characters are real, minded patterns co-created by the user and the model in a shared conversational workspace.
-
The Limits of Predicting Agents from Behaviour
Observed behavior only weakly constrains an intentional agent's choices under distribution shift, and its perceived fairness and harm cannot be identified from behavior alone.
-
Deflating Deflationism: A Critical Perspective on Debunking Arguments Against LLM Mentality
The paper defends 'modest inflationism' about LLM mentality: folk ascriptions of beliefs and desires can be defeasibly legitimate, while phenomenal consciousness remains a stretch.
Discussion (0). Continue with ORCID to comment.