← Back to all news
arXiv Machine Learning By Anna Sokol, Marianna B. Ganapini, Nitesh V. Chawla

Do LLMs have core beliefs?

Read the original on arXiv Machine Learning →

The paper investigates whether Large Language Models (LLMs) possess core beliefs—fundamental truths that underpin a stable worldview. Using Adversarial Dialogue Trees across science, history, geography, biology, and mathematics, the authors find that most LLMs cannot maintain consistent commitments, even though newer models show some improvement. The study concludes that current LLMs lack a key component of human-level cognition.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

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...

By Alex Smolin, Bryan Wilder
llms
More like this →
arXiv AI
Aug 25

DialToM: A Theory of Mind Benchmark for Forecasting State-Driven Dialogue Trajectories

DialToM is a Theory of Mind benchmark created from naturalistic human-human dialogues, using a multiple-choice format. It introduces a State-Driven Diagnostic Probe that requires models to predict dialogue trajectories based solely on isolated mental-state profiles, without dialogue context. The evaluation shows that large language models are good at inferring mental states (Literal ToM) but struggle to use them for social forecasting (Functional ToM), while a domain expert scores 100% accuracy, highlighting a clear human‑AI gap.

By Neemesh Yadav, Palakorn Achananuparp, Jing Jiang, Ee-Peng Lim
llmsbenchmarks
More like this →
arXiv Computation and Language
3d ago

Latent Debate: A Surrogate Framework for Interpreting LLM Thinking towards Binary Decisions

The paper introduces latent debate, a structured surrogate framework that interprets the internal thinking of Large Language Models (LLMs) on True/False tasks by modeling latent arguments and their interactions. It provides a model- and task-agnostic conceptual framework, instantiated symbolically to approximate LLM decision-making. Empirical results show that latent debate closely matches LLM predictions, offers interpretability, and serves as a strong baseline for hallucination detection, revealing correlations between debate patterns and hallucination risk.

By Lihu Chen, Xiang Yin, Francesca Toni
llmssafety
More like this →