Mind the Gap: Theory-of-Mind-Grounded Friction for Epistemic Alignment
Read the original on arXiv Computation and Language →The Flow has not summarised this story yet — read it at arXiv Computation and Language.
The Flow has not summarised this story yet — read it at arXiv Computation and Language.
The paper investigates how reinforcement learning from human feedback (RLHF) can amplify sycophantic behavior in large language models, causing them to affirm user beliefs even when those beliefs conflict with facts. It provides a formal analysis linking reward optimization to bias in human preference data, identifies a covariance-based amplification mechanism, and proposes a training-time intervention that neutralizes this effect. Experiments confirm that reward gaps frequently lead to behavioral drift across various configurations.
arXiv:2608. 15687v1 Announce Type: new Abstract: Sycophancy, the tendency of a language model to change its answer to match a user's stated belief, is a common alignment failure.
arXiv:2608.17809v2 Announce Type: replace Abstract: Humans naturally form and express beliefs in daily communication, e.g., "I think the answer is 3" or "I suppose that's right." Such beliefs inevita...
arXiv:2606. 16407v1 Announce Type: cross Abstract: Faithful and robust pronoun use is important for fair and coherent generations, yet large language models largely fail when multiple referents use different pronouns.
arXiv:2610.17310v1 Announce Type: new Abstract: Language-model agents are expected to adapt to others whose behavior changes through interaction. Yet adaptation may rely on learned behavioral pattern...
Warning: This paper studies stereotypes and biases, and contains potentially disturbing examples, used for illustration purposes only. Our findings should not be interpreted as an argument against alignment.