{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:QY52VFX5QPO3K5D3HNVIFIKGKP","short_pith_number":"pith:QY52VFX5","schema_version":"1.0","canonical_sha256":"863baa96fd83ddb5747b3b6a82a14653e96ead1387cf737d4793bba18e1aa9b4","source":{"kind":"arxiv","id":"2503.16094","version":1},"attestation_state":"computed","paper":{"title":"Cultural Alignment in Large Language Models Using Soft Prompt Tuning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Martin Ferianc, Miguel Rodrigues, Philip Treleaven, Reem I. Masoud","submitted_at":"2025-03-20T12:34:01Z","abstract_excerpt":"Large Language Model (LLM) alignment conventionally relies on supervised fine-tuning or reinforcement learning based alignment frameworks. These methods typically require labeled or preference datasets and involve updating model weights to align the LLM with the training objective or reward model. Meanwhile, in social sciences such as cross-cultural studies, factor analysis is widely used to uncover underlying dimensions or latent variables that explain observed patterns in survey data. The non-differentiable nature of these measurements deriving from survey data renders the former alignment m"},"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":"2503.16094","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-03-20T12:34:01Z","cross_cats_sorted":[],"title_canon_sha256":"ed5dbc11c11faceded6c1558edebc8b4d90e4ea60b88a2b23a67fc7d4220c6da","abstract_canon_sha256":"a8f5ae33be75a944a65c00450ece72b6edbe06c2b05facb743b0466125bf834c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:36:05.771710Z","signature_b64":"OLhilqWnrkQXw52WsRQSxtTakrBs76OxST93v1irsMA7KpP29wDMrPO9sK7fjgMsj/bJmEl+jROLNnDwCf2uDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"863baa96fd83ddb5747b3b6a82a14653e96ead1387cf737d4793bba18e1aa9b4","last_reissued_at":"2026-07-05T10:36:05.770938Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:36:05.770938Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Cultural Alignment in Large Language Models Using Soft Prompt Tuning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Martin Ferianc, Miguel Rodrigues, Philip Treleaven, Reem I. Masoud","submitted_at":"2025-03-20T12:34:01Z","abstract_excerpt":"Large Language Model (LLM) alignment conventionally relies on supervised fine-tuning or reinforcement learning based alignment frameworks. These methods typically require labeled or preference datasets and involve updating model weights to align the LLM with the training objective or reward model. Meanwhile, in social sciences such as cross-cultural studies, factor analysis is widely used to uncover underlying dimensions or latent variables that explain observed patterns in survey data. The non-differentiable nature of these measurements deriving from survey data renders the former alignment m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.16094","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/2503.16094/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":"2503.16094","created_at":"2026-07-05T10:36:05.771034+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.16094v1","created_at":"2026-07-05T10:36:05.771034+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.16094","created_at":"2026-07-05T10:36:05.771034+00:00"},{"alias_kind":"pith_short_12","alias_value":"QY52VFX5QPO3","created_at":"2026-07-05T10:36:05.771034+00:00"},{"alias_kind":"pith_short_16","alias_value":"QY52VFX5QPO3K5D3","created_at":"2026-07-05T10:36:05.771034+00:00"},{"alias_kind":"pith_short_8","alias_value":"QY52VFX5","created_at":"2026-07-05T10:36:05.771034+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.00242","citing_title":"Whispers of Many Shores: Cultural Alignment through Collaborative Cultural Expertise","ref_index":5,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QY52VFX5QPO3K5D3HNVIFIKGKP","json":"https://pith.science/pith/QY52VFX5QPO3K5D3HNVIFIKGKP.json","graph_json":"https://pith.science/api/pith-number/QY52VFX5QPO3K5D3HNVIFIKGKP/graph.json","events_json":"https://pith.science/api/pith-number/QY52VFX5QPO3K5D3HNVIFIKGKP/events.json","paper":"https://pith.science/paper/QY52VFX5"},"agent_actions":{"view_html":"https://pith.science/pith/QY52VFX5QPO3K5D3HNVIFIKGKP","download_json":"https://pith.science/pith/QY52VFX5QPO3K5D3HNVIFIKGKP.json","view_paper":"https://pith.science/paper/QY52VFX5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.16094&json=true","fetch_graph":"https://pith.science/api/pith-number/QY52VFX5QPO3K5D3HNVIFIKGKP/graph.json","fetch_events":"https://pith.science/api/pith-number/QY52VFX5QPO3K5D3HNVIFIKGKP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QY52VFX5QPO3K5D3HNVIFIKGKP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QY52VFX5QPO3K5D3HNVIFIKGKP/action/storage_attestation","attest_author":"https://pith.science/pith/QY52VFX5QPO3K5D3HNVIFIKGKP/action/author_attestation","sign_citation":"https://pith.science/pith/QY52VFX5QPO3K5D3HNVIFIKGKP/action/citation_signature","submit_replication":"https://pith.science/pith/QY52VFX5QPO3K5D3HNVIFIKGKP/action/replication_record"}},"created_at":"2026-07-05T10:36:05.771034+00:00","updated_at":"2026-07-05T10:36:05.771034+00:00"}