Readable, Faithful, Used: Three Dissociable Properties of Demographic Identity in a Language Model

Abstract:Large language models are widely used to simulate survey respondents, yet their answers are homogeneous and unfaithful to real inter-group differences. We ask where demographic group identity lives inside an LLM, how faithfully its geometry mirrors real inter-group opinion structure, and whether it uses what it encodes. Using representational similarity analysis against Pew ground truth over 169 demographic cells, we score 1,089 read-out locations in Mistral-7B and intervene causally across six attribute types. Four results. (1) The standard last-token residual read-out understates the model: attention-head read-outs dominate it in five of six types, with selection-corrected fidelity up to rho=0.63 -- roughly 70% of the measurement-reliability ceiling -- surviving a lexical-similarity control. (2) A single head (L11 H16) is significantly faithful in all six types as a fixed location, while race-based types stay weak and prompt-fragile. Both phenomena replicate -- the analogous head significant in five of six types, weakest on the same race type -- across three checkpoints of a second model family, where ten billion training tokens barely move the map. (3) Causal use does not follow fidelity: the clearest causal pathway sits in one of the least faithful types (p=0.002, cluster-robust, fixed depth), the most faithful type shows no correction-surviving single-layer effect, and replacing the entire identity moves predictions by under 2% of their error. (4) A 128-dimensional probe of the single head lands 21-31% closer to survey truth than the model's own answers -- yet recovers almost none of the per-question group ordering, no better than the answers themselves. Readable, faithfully arranged, and causally used are three dissociable properties of the same model; treating them as one claim is what keeps the "can LLMs simulate populations" debate unresolved.

Computation and Language (cs.CL)

arXiv:2608.18768 [cs.CL]

(or arXiv:2608.18768v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.18768 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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