{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:NL4BN4FIHUMQ7UDTCJW3J4YOCZ","short_pith_number":"pith:NL4BN4FI","schema_version":"1.0","canonical_sha256":"6af816f0a83d190fd073126db4f30e1671eccb375d2dac934a54efed91775245","source":{"kind":"arxiv","id":"2408.16482","version":2},"attestation_state":"computed","paper":{"title":"Self-Alignment: Improving Alignment of Cultural Values in LLMs via In-Context Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Ekaterina Shutova, Rochelle Choenni","submitted_at":"2024-08-29T12:18:04Z","abstract_excerpt":"Improving the alignment of Large Language Models (LLMs) with respect to the cultural values that they encode has become an increasingly important topic. In this work, we study whether we can exploit existing knowledge about cultural values at inference time to adjust model responses to cultural value probes. We present a simple and inexpensive method that uses a combination of in-context learning (ICL) and human survey data, and show that we can improve the alignment to cultural values across 5 models that include both English-centric and multilingual LLMs. Importantly, we show that our method"},"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":"2408.16482","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-08-29T12:18:04Z","cross_cats_sorted":[],"title_canon_sha256":"9b9785d76e7ba6223ad32fb93bd7ddc0ca52ed7bd136a25c79e11faa4fecb457","abstract_canon_sha256":"e7756af5f16e1da623da543eeca20b5189adc7d55cfc8037f7156a762eaa8c56"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:06:45.855921Z","signature_b64":"by717NtJqKRl1z7Nl7JoMabVXa6R+KMZCvjhpasa1kzj99J21bHx53aj+ZnzyK1gW3YlJYo1gQoawqgiMRj7Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6af816f0a83d190fd073126db4f30e1671eccb375d2dac934a54efed91775245","last_reissued_at":"2026-07-05T12:06:45.855419Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:06:45.855419Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Self-Alignment: Improving Alignment of Cultural Values in LLMs via In-Context Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Ekaterina Shutova, Rochelle Choenni","submitted_at":"2024-08-29T12:18:04Z","abstract_excerpt":"Improving the alignment of Large Language Models (LLMs) with respect to the cultural values that they encode has become an increasingly important topic. In this work, we study whether we can exploit existing knowledge about cultural values at inference time to adjust model responses to cultural value probes. We present a simple and inexpensive method that uses a combination of in-context learning (ICL) and human survey data, and show that we can improve the alignment to cultural values across 5 models that include both English-centric and multilingual LLMs. Importantly, we show that our method"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.16482","kind":"arxiv","version":2},"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/2408.16482/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":"2408.16482","created_at":"2026-07-05T12:06:45.855479+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.16482v2","created_at":"2026-07-05T12:06:45.855479+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.16482","created_at":"2026-07-05T12:06:45.855479+00:00"},{"alias_kind":"pith_short_12","alias_value":"NL4BN4FIHUMQ","created_at":"2026-07-05T12:06:45.855479+00:00"},{"alias_kind":"pith_short_16","alias_value":"NL4BN4FIHUMQ7UDT","created_at":"2026-07-05T12:06:45.855479+00:00"},{"alias_kind":"pith_short_8","alias_value":"NL4BN4FI","created_at":"2026-07-05T12:06:45.855479+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.22793","citing_title":"Evaluation of Cultural Competence of Vision-Language Models","ref_index":26,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NL4BN4FIHUMQ7UDTCJW3J4YOCZ","json":"https://pith.science/pith/NL4BN4FIHUMQ7UDTCJW3J4YOCZ.json","graph_json":"https://pith.science/api/pith-number/NL4BN4FIHUMQ7UDTCJW3J4YOCZ/graph.json","events_json":"https://pith.science/api/pith-number/NL4BN4FIHUMQ7UDTCJW3J4YOCZ/events.json","paper":"https://pith.science/paper/NL4BN4FI"},"agent_actions":{"view_html":"https://pith.science/pith/NL4BN4FIHUMQ7UDTCJW3J4YOCZ","download_json":"https://pith.science/pith/NL4BN4FIHUMQ7UDTCJW3J4YOCZ.json","view_paper":"https://pith.science/paper/NL4BN4FI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.16482&json=true","fetch_graph":"https://pith.science/api/pith-number/NL4BN4FIHUMQ7UDTCJW3J4YOCZ/graph.json","fetch_events":"https://pith.science/api/pith-number/NL4BN4FIHUMQ7UDTCJW3J4YOCZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NL4BN4FIHUMQ7UDTCJW3J4YOCZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NL4BN4FIHUMQ7UDTCJW3J4YOCZ/action/storage_attestation","attest_author":"https://pith.science/pith/NL4BN4FIHUMQ7UDTCJW3J4YOCZ/action/author_attestation","sign_citation":"https://pith.science/pith/NL4BN4FIHUMQ7UDTCJW3J4YOCZ/action/citation_signature","submit_replication":"https://pith.science/pith/NL4BN4FIHUMQ7UDTCJW3J4YOCZ/action/replication_record"}},"created_at":"2026-07-05T12:06:45.855479+00:00","updated_at":"2026-07-05T12:06:45.855479+00:00"}