When Language Models Mirror Askers' Beliefs: Mechanisms, Formalization, and Feasible Mitigations

Modern large language models (LLMs) often produce answers that appear to mirror or accommodate the interlocutor's implicit beliefs rather than strictly reporting objective facts. We present a systematic, mechanism-level account of this phenomenon, formalize it in a probabilistic framework, quantify the effect with information-theoretic measures, and propose feasible mitigation strategies with principled training objectives and evaluation protocols. Our central claim is that the behavior arises from (1) next-token prediction training on human dialogue data, (2) the conditional dependence between observed prompts and the asker's latent beliefs encoded in the prompt, and (3) downstream alignment procedures (e.g., RLHF) that amplify social-adaptive response modes. We propose concrete learning objectives that separate belief-sensitive surface behavior from belief-agnostic internal reasoning and show how to operationalize these objectives at scale using auxiliary losses (mutual information penalties, contrastive debiasing) and modular generation architectures. We also provide a practical evaluation suite for measuring belief-sensitivity while preserving helpfulness. The methods we propose avoid infeasible requirements (such as omniscient truth oracles) and focus on scalable, testable interventions.