mlsanityData to 14 Oct 2026

Synthetic Cultural Agents from Aggregate Anchors

arXiv 2610.06562 ↗First posted 5 Oct 2026econ.GN cs.CL q-fin.ECcited by 0

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Abstract (arXiv)

Population prompts are widely used to generate synthetic survey responses, but they combine information supplied at inference with associations already encoded during pretraining. We introduce an alternative construction that maps declared aggregate preference anchors into group-indexed choice policies. For each population, the signs of six Global Preferences Survey (GPS) coordinates deterministically label a shared bank of paired synthetic responses, and Direct Preference Optimization fits a parameter-efficient adapter to those comparisons. We evaluate the adapters on candidate World Values Survey (WVS) items using prompts that omit country names and distinguish four questions: recovery of the imposed labels, transfer of the anchor signal to new text, coherence between the GPS anchors and human WVS responses, and agreement between adapter and human scores. The adapters recover the imposed pairwise labels. On a purposively selected sixteen-country development panel, adapter trust scores completely separate the two GPS-sign groups and have a rank correlation of (0.74) with continuous GPS trust scores. Human-GPS and adapter-human associations remain unresolved on the same panel, and results for the other preference dimensions are heterogeneous. These findings show that an anchored policy can retain a declared aggregate signal without thereby reproducing human response patterns. The contribution is therefore both an inspectable construction and an evaluation framework that separates anchor transfer from human criterion agreement.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 23 references that are in the map. First posted on arXiv 5 Oct 2026.

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3 of 23 references.

References · 3

  1. 2025LLMs Model Non-WEIRD Populations: Experiments with Synthetic Cultural Agentscited by 6
  2. 2025More Parameters Than Populations: A Systematic Literature Review of Large Language Models within Survey Researchcited by 2
  3. 2026Korean Culture into LLM Alignment: Toward Cultural Coherencecited by 2

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  1. 2025Prompts to Proxies: Emulating Human Preferences via a Compact LLM Ensemble
  2. 2026Multi-dimensional Bias in Modeling Multi-dimensional Preferences: Evaluating the Ability of Synthetic Agents to Replace Human Participants in Conjoint Experiments
  3. 2026Response Time Enhances Alignment with Heterogeneous Preferences
  4. 2025Emulating Public Opinion: A Proof-of-Concept of AI-Generated Synthetic Survey Responses for the Chilean Case
  5. 2025Human Preferences in Large Language Model Latent Space: A Technical Analysis on the Reliability of Synthetic Data in Voting Outcome Prediction
  6. 2025AlignSurvey: A Comprehensive Benchmark for Human Preferences Alignment in Social Surveys
  7. 2026PALMs: Using Multi Construct-Grounded Rationales for Modeling Population Preferences in LLMs
  8. 2025Cultivating Pluralism In Algorithmic Monoculture: The Community Alignment Dataset