{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:AJZ5K657ZDYVLSJOZZPVE2EE64","short_pith_number":"pith:AJZ5K657","schema_version":"1.0","canonical_sha256":"0273d57bbfc8f155c92ece5f526884f73d5e9ad473d9f3049d5edfbc97b03a63","source":{"kind":"arxiv","id":"2506.21881","version":1},"attestation_state":"computed","paper":{"title":"A Dual-Layered Evaluation of Geopolitical and Cultural Bias in LLMs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Hyuhng Joon Kim, Sean Kim","submitted_at":"2025-06-27T03:37:15Z","abstract_excerpt":"As large language models (LLMs) are increasingly deployed across diverse linguistic and cultural contexts, understanding their behavior in both factual and disputable scenarios is essential, especially when their outputs may shape public opinion or reinforce dominant narratives. In this paper, we define two types of bias in LLMs: model bias (bias stemming from model training) and inference bias (bias induced by the language of the query), through a two-phase evaluation. Phase 1 evaluates LLMs on factual questions where a single verifiable answer exists, assessing whether models maintain consis"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2506.21881","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-06-27T03:37:15Z","cross_cats_sorted":[],"title_canon_sha256":"f377e18b7f6ccc18780242c09a6e83d59f77849b75e26bb50c58b42bda7bb751","abstract_canon_sha256":"c160a64d40655a02355a5030d004768a54c530c01bd1201667d0e0f0fca98abb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:28:03.354771Z","signature_b64":"VtnwEaxEdiwT2tyjvjOA/+D7xGEQ65Y2MCDHtJbrsA85FbtcSejydBWlfH+x4mButbgHPVTumyA/QjVRH6qHAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0273d57bbfc8f155c92ece5f526884f73d5e9ad473d9f3049d5edfbc97b03a63","last_reissued_at":"2026-07-05T11:28:03.354276Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:28:03.354276Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Dual-Layered Evaluation of Geopolitical and Cultural Bias in LLMs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Hyuhng Joon Kim, Sean Kim","submitted_at":"2025-06-27T03:37:15Z","abstract_excerpt":"As large language models (LLMs) are increasingly deployed across diverse linguistic and cultural contexts, understanding their behavior in both factual and disputable scenarios is essential, especially when their outputs may shape public opinion or reinforce dominant narratives. In this paper, we define two types of bias in LLMs: model bias (bias stemming from model training) and inference bias (bias induced by the language of the query), through a two-phase evaluation. Phase 1 evaluates LLMs on factual questions where a single verifiable answer exists, assessing whether models maintain consis"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.21881","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2506.21881/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2506.21881","created_at":"2026-07-05T11:28:03.354351+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.21881v1","created_at":"2026-07-05T11:28:03.354351+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.21881","created_at":"2026-07-05T11:28:03.354351+00:00"},{"alias_kind":"pith_short_12","alias_value":"AJZ5K657ZDYV","created_at":"2026-07-05T11:28:03.354351+00:00"},{"alias_kind":"pith_short_16","alias_value":"AJZ5K657ZDYVLSJO","created_at":"2026-07-05T11:28:03.354351+00:00"},{"alias_kind":"pith_short_8","alias_value":"AJZ5K657","created_at":"2026-07-05T11:28:03.354351+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AJZ5K657ZDYVLSJOZZPVE2EE64","json":"https://pith.science/pith/AJZ5K657ZDYVLSJOZZPVE2EE64.json","graph_json":"https://pith.science/api/pith-number/AJZ5K657ZDYVLSJOZZPVE2EE64/graph.json","events_json":"https://pith.science/api/pith-number/AJZ5K657ZDYVLSJOZZPVE2EE64/events.json","paper":"https://pith.science/paper/AJZ5K657"},"agent_actions":{"view_html":"https://pith.science/pith/AJZ5K657ZDYVLSJOZZPVE2EE64","download_json":"https://pith.science/pith/AJZ5K657ZDYVLSJOZZPVE2EE64.json","view_paper":"https://pith.science/paper/AJZ5K657","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.21881&json=true","fetch_graph":"https://pith.science/api/pith-number/AJZ5K657ZDYVLSJOZZPVE2EE64/graph.json","fetch_events":"https://pith.science/api/pith-number/AJZ5K657ZDYVLSJOZZPVE2EE64/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AJZ5K657ZDYVLSJOZZPVE2EE64/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AJZ5K657ZDYVLSJOZZPVE2EE64/action/storage_attestation","attest_author":"https://pith.science/pith/AJZ5K657ZDYVLSJOZZPVE2EE64/action/author_attestation","sign_citation":"https://pith.science/pith/AJZ5K657ZDYVLSJOZZPVE2EE64/action/citation_signature","submit_replication":"https://pith.science/pith/AJZ5K657ZDYVLSJOZZPVE2EE64/action/replication_record"}},"created_at":"2026-07-05T11:28:03.354351+00:00","updated_at":"2026-07-05T11:28:03.354351+00:00"}