{"paper":{"title":"Diagnosing Korean-Language LLM Political Bias via Census-Grounded Agent Simulation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Census-grounded agent simulations diagnose political biases in Korean LLMs and accurately predict real election outcomes.","cross_cats":["cs.AI"],"primary_cat":"cs.CY","authors_text":"Sungwoo Kang","submitted_at":"2026-05-18T13:42:23Z","abstract_excerpt":"Large language models (LLMs) exhibit systematic political biases in voter simulations, but their underlying mechanisms and cross-lingual generalizations remain poorly understood. We introduce Dynamo-K, a census-grounded simulation framework evaluating Korean-language LLM political behavior across four models on six Korean elections (2017-2025). Using this framework, we identify three systematic failure modes: (1) progressive bias in moderate agents, where explicit mitigation reduces Mean Absolute Error (MAE) by 5.2 times; (2) model-dependent third-party salience collapse, distinguishing betwee"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Dynamo-K accurately predicts 3/3 presidential winners including a 2.1%p MAE on the 2022 race with 0.73%p margin and correctly identifies the dominant party in a held-out local election.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The assumption that census-grounded agent profiles and scenario prompts produce voter behavior distributions that are sufficiently representative of actual Korean electorate responses for the purpose of diagnosing model bias.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Dynamo-K is a census-grounded agent simulation that diagnoses three failure modes in Korean LLMs' political predictions and shows calibration methods that improve accuracy on historical elections.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Census-grounded agent simulations diagnose political biases in Korean LLMs and accurately predict real election outcomes.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"57d98320053fdc2886d69f35331bb58df11be85062a97fd82d8427d4e54f5842"},"source":{"id":"2605.18395","kind":"arxiv","version":1},"verdict":{"id":"5b578aa0-c5dd-40dd-a10c-1569634e819b","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T23:59:58.839206Z","strongest_claim":"Dynamo-K accurately predicts 3/3 presidential winners including a 2.1%p MAE on the 2022 race with 0.73%p margin and correctly identifies the dominant party in a held-out local election.","one_line_summary":"Dynamo-K is a census-grounded agent simulation that diagnoses three failure modes in Korean LLMs' political predictions and shows calibration methods that improve accuracy on historical elections.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The assumption that census-grounded agent profiles and scenario prompts produce voter behavior distributions that are sufficiently representative of actual Korean electorate responses for the purpose of diagnosing model bias.","pith_extraction_headline":"Census-grounded agent simulations diagnose political biases in Korean LLMs and accurately predict real election outcomes."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.18395/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"cited_work_retraction","ran_at":"2026-05-19T23:52:10.869245Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"citation_quote_validity","ran_at":"2026-05-19T23:50:03.292214Z","status":"skipped","version":"0.1.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T23:33:29.753183Z","status":"skipped","version":"1.0.0","findings_count":0},{"name":"external_links","ran_at":"2026-05-19T23:31:43.709662Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T23:21:58.731675Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"03335653e9ddc7aca798710bc4b3b103fa45494c82f185060ecb24cce75fba1c"},"references":{"count":19,"sample":[{"doi":"","year":2026,"title":"Dynamo: Large-scale political simulation with LLM agents","work_id":"","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2023,"title":"L. P. Argyle, E. C. Busby, N. Fulda, J. R. Gubler, C. Rytting, and D. Wingate. Out of one, many: Using language models to simulate human samples.Political Analysis, 31(3):337–351, 2023","work_id":"","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1017/jea.2024.11","year":2022,"title":"M. D. Jenkins and H. J. Kim. The role of misogyny in the 2022 Korean presidential election: Under- standing the backlash against feminism in industrialized democracies.Journal of East Asian Studies, 2","work_id":"","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2023,"title":"S. Feng, C. Y. Park, Y. Liu, and Y. Tsvetkov. From pretraining data to language models to downstream tasks: Tracking the trails of political biases leading to unfair NLP models. InProceedings of the 6","work_id":"","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2000,"title":"G. Deffuant, D. Neau, F. Amblard, and G. Weisbuch. 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