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SRC-510b20399598128b · Source record

Specializing Large Language Models to Simulate Survey Response Distributions for Global Populations

ACL Anthology / NAACL authors · 6 connected claims

D01 · Marketing, Brand & Revenue SystemsD03 · Intelligence, Research & Decision ProductsD11 · AI Models, Agents & Human–AI InteractionD12 · Data, Knowledge, Search & EvidenceD19 · Measurement, Experimentation & EconomicsD20 · Consumer, Culture & Lifestyle Futures

Canonical route

https://aclanthology.org/2025.naacl-long.162/

Claims connected to this source

  1. MWITA-SR-2026-011The global-distribution benchmark used the 2017–2022 World Values Survey covering 66 countries and more than 80,000 human respondents.Evidence A
  2. MWITA-SR-2026-012Fine-tuning on response distributions outperformed tested prompting and zero-shot baselines on seen and unseen benchmark splits.Evidence A
  3. MWITA-SR-2026-013Even the best specialized models remained far from perfect, particularly on unseen questions.Evidence A
  4. MWITA-SR-2026-014Tested LLMs produced less cross-country diversity than the human survey data.Evidence A
  5. MWITA-SR-2026-068Temporal validation is required because a model calibrated to a past survey wave can drift from current preferences even if its original benchmark passed.Evidence C
  6. MWITA-SR-2026-075No source in this bundle demonstrates an independently audited commercial decision outcome caused by synthetic-consumer evidence.Evidence D

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