{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:DSL7D2WCIDD27GTMYCBFOJVKUY","short_pith_number":"pith:DSL7D2WC","schema_version":"1.0","canonical_sha256":"1c97f1eac240c7af9a6cc0825726aaa61d010c919ba9218279f127ac738d287a","source":{"kind":"arxiv","id":"2504.02953","version":1},"attestation_state":"computed","paper":{"title":"Cultural Learning-Based Culture Adaptation of Language Models","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Anna Korhonen, Chen Cecilia Liu, Iryna Gurevych","submitted_at":"2025-04-03T18:16:26Z","abstract_excerpt":"Adapting large language models (LLMs) to diverse cultural values is a challenging task, as existing LLMs often reflect the values of specific groups by default, and potentially causing harm to others. In this paper, we present CLCA, a novel framework for enhancing LLM alignment with cultural values based on cultural learning. The framework leverages simulated social interactions to generate conversations in which LLMs engage in role-playing within culturally adapted social scenarios, capturing implicit cultural norms for model fine-tuning. CLCA improves cultural value alignment across various "},"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":"2504.02953","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2025-04-03T18:16:26Z","cross_cats_sorted":[],"title_canon_sha256":"f9552e2a044486f5a989c45e0da320fa48539358eea30d41e42b8a6f04f76b4e","abstract_canon_sha256":"9d0f963c4ee822f11af10304604e94fdc417c0c00e49af28144d244e26d7511c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:44:05.574330Z","signature_b64":"75U8yuoNdvPHg93CHM/fBCFOEisbyWO8N6i5wxGIcqOr7k4bmvS8OwU6xw7Zf3RXfa1NG2iPyVdnM8gNa2BiBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1c97f1eac240c7af9a6cc0825726aaa61d010c919ba9218279f127ac738d287a","last_reissued_at":"2026-07-05T10:44:05.573809Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:44:05.573809Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Cultural Learning-Based Culture Adaptation of Language Models","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Anna Korhonen, Chen Cecilia Liu, Iryna Gurevych","submitted_at":"2025-04-03T18:16:26Z","abstract_excerpt":"Adapting large language models (LLMs) to diverse cultural values is a challenging task, as existing LLMs often reflect the values of specific groups by default, and potentially causing harm to others. In this paper, we present CLCA, a novel framework for enhancing LLM alignment with cultural values based on cultural learning. The framework leverages simulated social interactions to generate conversations in which LLMs engage in role-playing within culturally adapted social scenarios, capturing implicit cultural norms for model fine-tuning. CLCA improves cultural value alignment across various "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.02953","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/2504.02953/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":"2504.02953","created_at":"2026-07-05T10:44:05.573868+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.02953v1","created_at":"2026-07-05T10:44:05.573868+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.02953","created_at":"2026-07-05T10:44:05.573868+00:00"},{"alias_kind":"pith_short_12","alias_value":"DSL7D2WCIDD2","created_at":"2026-07-05T10:44:05.573868+00:00"},{"alias_kind":"pith_short_16","alias_value":"DSL7D2WCIDD27GTM","created_at":"2026-07-05T10:44:05.573868+00:00"},{"alias_kind":"pith_short_8","alias_value":"DSL7D2WC","created_at":"2026-07-05T10:44:05.573868+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.01671","citing_title":"When Meaning Travels: A Granular Lens on Hybrid-MoE's Role in Idiomatic Understanding for Language Models","ref_index":113,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DSL7D2WCIDD27GTMYCBFOJVKUY","json":"https://pith.science/pith/DSL7D2WCIDD27GTMYCBFOJVKUY.json","graph_json":"https://pith.science/api/pith-number/DSL7D2WCIDD27GTMYCBFOJVKUY/graph.json","events_json":"https://pith.science/api/pith-number/DSL7D2WCIDD27GTMYCBFOJVKUY/events.json","paper":"https://pith.science/paper/DSL7D2WC"},"agent_actions":{"view_html":"https://pith.science/pith/DSL7D2WCIDD27GTMYCBFOJVKUY","download_json":"https://pith.science/pith/DSL7D2WCIDD27GTMYCBFOJVKUY.json","view_paper":"https://pith.science/paper/DSL7D2WC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.02953&json=true","fetch_graph":"https://pith.science/api/pith-number/DSL7D2WCIDD27GTMYCBFOJVKUY/graph.json","fetch_events":"https://pith.science/api/pith-number/DSL7D2WCIDD27GTMYCBFOJVKUY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DSL7D2WCIDD27GTMYCBFOJVKUY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DSL7D2WCIDD27GTMYCBFOJVKUY/action/storage_attestation","attest_author":"https://pith.science/pith/DSL7D2WCIDD27GTMYCBFOJVKUY/action/author_attestation","sign_citation":"https://pith.science/pith/DSL7D2WCIDD27GTMYCBFOJVKUY/action/citation_signature","submit_replication":"https://pith.science/pith/DSL7D2WCIDD27GTMYCBFOJVKUY/action/replication_record"}},"created_at":"2026-07-05T10:44:05.573868+00:00","updated_at":"2026-07-05T10:44:05.573868+00:00"}