arXiv AI
Do Large Language Models Possess a Theory of Mind? A Comparative Evaluation Using the Strange Stories Paradigm
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arXiv AI
2d ago
ToMStories: Interpreting Theory-of-Mind in Synthetic Narratives
The paper introduces ToMStories, a dataset of about 6,000 synthetic narrative vignettes designed to test language models’ ability to infer characters’ psychological states such as emotions, fears, motivations, and wounds. Each vignette is paired with either a multiple-choice or open-ended question, and the states are presented implicitly, explicitly, or with conflicting cues. The authors evaluate thirteen open-weight models ranging from 1B to 72B parameters, finding that while the largest models perform on par with humans on multiple-choice tasks, they lag on open-ended questions, revealing simplifications in how models interpret and generate implicit psychological states.
arXiv AI
Aug 13
Social Meaning in Large Language Models: Structure, Magnitude, and Pragmatic Prompting
arXiv:2604. 02512v2 Announce Type: replace-cross Abstract: Large language models (LLMs) increasingly exhibit human-like patterns of pragmatic and social reasoning.
arXiv AI
Jul 7
The Rise of Verbal Tics in Large Language Models: A Systematic Analysis Across Frontier Models
arXiv:2604. 19139v3 Announce Type: replace-cross Abstract: As Large Language Models (LLMs) continue to evolve through alignment techniques such as Reinforcement Learning from Human Feedback (RLHF) and Constitutional AI, a growing and increasingly conspicuous phenomenon has emerged: the proliferation of verbal tics--repetitive, formulaic linguistic patterns that pervade model outputs.
arXiv AI
Sep 10
Beliefs and Behavior in Language Models
arXiv:2609.07943v1 Announce Type: new Abstract: There is significant uncertainty about whether abstractions like beliefs or desires usefully describe the behavior of large language models (LLMs). In...
arXiv AI
6d ago
Jev-ToM: Evaluating Theory of Mind in Typed Decision Models
arXiv:2610.18610v1 Announce Type: cross Abstract: Dialogue and collaborative assistants repeatedly judge what a partner knows, believes, or intends. Typed decision models offer a potential alternativ...
arXiv AI
Aug 20
Do Large Language Models Hallucinate Electric Fata Morganas?
The paper investigates why large language models (LLMs) produce hallucinations—outputs that are fabricated, unverifiable, or contradictory to source material—and argues that these hallucinations have philosophical implications for machine consciousness. It reviews known causes such as source‑target divergence, training‑inference discrepancies, and overfitting, and presents two empirical studies: one showing that higher temperature settings in GPT models yield plausible but incorrect answers, while lower temperatures produce accurate ones; and another demonstrating that an encoder‑only model trained on encyclopedic data answers factually without embellishment, suggesting hallucinations arise from exposure to subjective, socially diverse data rather than cognitive ability. Drawing on Turing, Searle’s Chinese Room, the frame problem, and cybernetic theory, the authors contend that a model’s self‑reports of emotion or sentience fall within the definition of hallucination, implying that any future machine consciousness may remain epistemically inaccessible because it would be indistinguishable from an advanced hallucination.