Reach Into The CHOIR: Free-List Elicitation Uncovers Distinct Model Voices in LLM Ensembles

Abstract:Open-ended LLM homogeneity can create false plurality when several systems appear to offer independent perspectives while returning the same familiar default. Single-pass answers obscure the distinction between agreement produced by a tightly constrained answer space, prompt-vocabulary echo, and broader answer spaces with stable alternatives beneath the surface. We introduce CHOIR (Collective Hierarchically-Ordered Inquiry Responses), a framework that adapts free-list elicitation from cognitive anthropology to LLM ensembles. CHOIR repeatedly elicits ranked lists, clusters items into prompt-level concepts, and measures concept salience across models, prompt variants, and persona conditions. We evaluate CHOIR on Infinity-Chat 100, an external prompt bank from recent work on open-ended model homogeneity, and on a 27-question targeted diagnostic bank designed to isolate mechanism-level contrasts. On Infinity-Chat 100, CHOIR reproduces high surface agreement (93/100 prompts above chance) while separating narrow prompts from broad prompts with recoverable depth. Across targeted probes and the external prompt bank, base-model identity remains the strongest recoverable signature, and persona prompts shift surfaced concepts within base-model signatures. A source-blind ranking module prioritises rare-but-stable candidates for later inspection. CHOIR turns open-ended homogeneity into a diagnostic measurement problem by asking where models converge, why they converge, and what remains reachable under structured depth probing.

23 pages, 6 figures. Published in the Proceedings of the Third Conference on Language Modeling (COLM 2026)

Artificial Intelligence (cs.AI); Computation and Language (cs.CL)

arXiv:2609.38448 [cs.AI]

(or arXiv:2609.38448v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.38448 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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