{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:5BKS5BRGJVL2YUTNQH7IH7L2QT","short_pith_number":"pith:5BKS5BRG","schema_version":"1.0","canonical_sha256":"e8552e86264d57ac526d81fe83fd7a84ff9efdbb996fe3c48b5fcaaa13603785","source":{"kind":"arxiv","id":"2406.04143","version":1},"attestation_state":"computed","paper":{"title":"Do Language Models Understand Morality? Towards a Robust Detection of Moral Content","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Aldo Gangemi, Luana Bulla, Misael Mongiov\\`i","submitted_at":"2024-06-06T15:08:16Z","abstract_excerpt":"The task of detecting moral values in text has significant implications in various fields, including natural language processing, social sciences, and ethical decision-making. Previously proposed supervised models often suffer from overfitting, leading to hyper-specialized moral classifiers that struggle to perform well on data from different domains. To address this issue, we introduce novel systems that leverage abstract concepts and common-sense knowledge acquired from Large Language Models and Natural Language Inference models during previous stages of training on multiple data sources. By"},"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":"2406.04143","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-06-06T15:08:16Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"4018b3e0df380e37845307e4534d5054a9c8511af575ed3e5fc6aca48138e0ba","abstract_canon_sha256":"2eb4313f310afcddd07fed42e829431f50d9415ffdb8991ce3439a95672daa02"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:28:21.256631Z","signature_b64":"i0XQB9xraooUGxVMa7m1OjzyBTf8sDkBidc3ePC8oKqRE5Q4LrnGXR9MYspWtLerh94oN5TnG/tTrDjvv82yCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e8552e86264d57ac526d81fe83fd7a84ff9efdbb996fe3c48b5fcaaa13603785","last_reissued_at":"2026-07-05T08:28:21.256144Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:28:21.256144Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Do Language Models Understand Morality? Towards a Robust Detection of Moral Content","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Aldo Gangemi, Luana Bulla, Misael Mongiov\\`i","submitted_at":"2024-06-06T15:08:16Z","abstract_excerpt":"The task of detecting moral values in text has significant implications in various fields, including natural language processing, social sciences, and ethical decision-making. Previously proposed supervised models often suffer from overfitting, leading to hyper-specialized moral classifiers that struggle to perform well on data from different domains. To address this issue, we introduce novel systems that leverage abstract concepts and common-sense knowledge acquired from Large Language Models and Natural Language Inference models during previous stages of training on multiple data sources. By"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.04143","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/2406.04143/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":"2406.04143","created_at":"2026-07-05T08:28:21.256200+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.04143v1","created_at":"2026-07-05T08:28:21.256200+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.04143","created_at":"2026-07-05T08:28:21.256200+00:00"},{"alias_kind":"pith_short_12","alias_value":"5BKS5BRGJVL2","created_at":"2026-07-05T08:28:21.256200+00:00"},{"alias_kind":"pith_short_16","alias_value":"5BKS5BRGJVL2YUTN","created_at":"2026-07-05T08:28:21.256200+00:00"},{"alias_kind":"pith_short_8","alias_value":"5BKS5BRG","created_at":"2026-07-05T08:28:21.256200+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2502.02074","citing_title":"Rethinking stance detection: A theoretically-informed research agenda for user-level inference using language models","ref_index":2332,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5BKS5BRGJVL2YUTNQH7IH7L2QT","json":"https://pith.science/pith/5BKS5BRGJVL2YUTNQH7IH7L2QT.json","graph_json":"https://pith.science/api/pith-number/5BKS5BRGJVL2YUTNQH7IH7L2QT/graph.json","events_json":"https://pith.science/api/pith-number/5BKS5BRGJVL2YUTNQH7IH7L2QT/events.json","paper":"https://pith.science/paper/5BKS5BRG"},"agent_actions":{"view_html":"https://pith.science/pith/5BKS5BRGJVL2YUTNQH7IH7L2QT","download_json":"https://pith.science/pith/5BKS5BRGJVL2YUTNQH7IH7L2QT.json","view_paper":"https://pith.science/paper/5BKS5BRG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.04143&json=true","fetch_graph":"https://pith.science/api/pith-number/5BKS5BRGJVL2YUTNQH7IH7L2QT/graph.json","fetch_events":"https://pith.science/api/pith-number/5BKS5BRGJVL2YUTNQH7IH7L2QT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5BKS5BRGJVL2YUTNQH7IH7L2QT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5BKS5BRGJVL2YUTNQH7IH7L2QT/action/storage_attestation","attest_author":"https://pith.science/pith/5BKS5BRGJVL2YUTNQH7IH7L2QT/action/author_attestation","sign_citation":"https://pith.science/pith/5BKS5BRGJVL2YUTNQH7IH7L2QT/action/citation_signature","submit_replication":"https://pith.science/pith/5BKS5BRGJVL2YUTNQH7IH7L2QT/action/replication_record"}},"created_at":"2026-07-05T08:28:21.256200+00:00","updated_at":"2026-07-05T08:28:21.256200+00:00"}