{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:7BP75INCHSY3OERSMDLKMAV2GW","short_pith_number":"pith:7BP75INC","schema_version":"1.0","canonical_sha256":"f85ffea1a23cb1b7123260d6a602ba35b4ef2ae91a0ed504a3ee96fb03458b48","source":{"kind":"arxiv","id":"2308.05596","version":1},"attestation_state":"computed","paper":{"title":"You Only Prompt Once: On the Capabilities of Prompt Learning on Large Language Models to Tackle Toxic Content","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SI"],"primary_cat":"cs.CL","authors_text":"Savvas Zannettou, Xinlei He, Yang Zhang, Yun Shen","submitted_at":"2023-08-10T14:14:13Z","abstract_excerpt":"The spread of toxic content online is an important problem that has adverse effects on user experience online and in our society at large. Motivated by the importance and impact of the problem, research focuses on developing solutions to detect toxic content, usually leveraging machine learning (ML) models trained on human-annotated datasets. While these efforts are important, these models usually do not generalize well and they can not cope with new trends (e.g., the emergence of new toxic terms). Currently, we are witnessing a shift in the approach to tackling societal issues online, particu"},"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":"2308.05596","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-08-10T14:14:13Z","cross_cats_sorted":["cs.SI"],"title_canon_sha256":"fbea4b19d1a290c195467ef69dafab157e8c4ce78fa25b929cbcff8ba5c25163","abstract_canon_sha256":"e441643517d200b5c817574043fc6dc3e271acdff84ed933a507688875576d42"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:40:02.820421Z","signature_b64":"YZC+Z4+A4aC4WeUOZKtxHIz317Y+FA21YupfuY1Gdb6+8tohtuPgXU8U5GFpvim/Bx9JAhGluKf3p7h8vwn+DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f85ffea1a23cb1b7123260d6a602ba35b4ef2ae91a0ed504a3ee96fb03458b48","last_reissued_at":"2026-07-05T06:40:02.819938Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:40:02.819938Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"You Only Prompt Once: On the Capabilities of Prompt Learning on Large Language Models to Tackle Toxic Content","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SI"],"primary_cat":"cs.CL","authors_text":"Savvas Zannettou, Xinlei He, Yang Zhang, Yun Shen","submitted_at":"2023-08-10T14:14:13Z","abstract_excerpt":"The spread of toxic content online is an important problem that has adverse effects on user experience online and in our society at large. Motivated by the importance and impact of the problem, research focuses on developing solutions to detect toxic content, usually leveraging machine learning (ML) models trained on human-annotated datasets. While these efforts are important, these models usually do not generalize well and they can not cope with new trends (e.g., the emergence of new toxic terms). Currently, we are witnessing a shift in the approach to tackling societal issues online, particu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.05596","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/2308.05596/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":"2308.05596","created_at":"2026-07-05T06:40:02.819995+00:00"},{"alias_kind":"arxiv_version","alias_value":"2308.05596v1","created_at":"2026-07-05T06:40:02.819995+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.05596","created_at":"2026-07-05T06:40:02.819995+00:00"},{"alias_kind":"pith_short_12","alias_value":"7BP75INCHSY3","created_at":"2026-07-05T06:40:02.819995+00:00"},{"alias_kind":"pith_short_16","alias_value":"7BP75INCHSY3OERS","created_at":"2026-07-05T06:40:02.819995+00:00"},{"alias_kind":"pith_short_8","alias_value":"7BP75INC","created_at":"2026-07-05T06:40:02.819995+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2507.05660","citing_title":"Optimus: A Robust Defense Framework for Mitigating Toxicity while Fine-Tuning Conversational AI","ref_index":29,"is_internal_anchor":false},{"citing_arxiv_id":"2604.12770","citing_title":"Teaching LLMs Human-Like Editing of Inappropriate Argumentation via Reinforcement Learning","ref_index":13,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7BP75INCHSY3OERSMDLKMAV2GW","json":"https://pith.science/pith/7BP75INCHSY3OERSMDLKMAV2GW.json","graph_json":"https://pith.science/api/pith-number/7BP75INCHSY3OERSMDLKMAV2GW/graph.json","events_json":"https://pith.science/api/pith-number/7BP75INCHSY3OERSMDLKMAV2GW/events.json","paper":"https://pith.science/paper/7BP75INC"},"agent_actions":{"view_html":"https://pith.science/pith/7BP75INCHSY3OERSMDLKMAV2GW","download_json":"https://pith.science/pith/7BP75INCHSY3OERSMDLKMAV2GW.json","view_paper":"https://pith.science/paper/7BP75INC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2308.05596&json=true","fetch_graph":"https://pith.science/api/pith-number/7BP75INCHSY3OERSMDLKMAV2GW/graph.json","fetch_events":"https://pith.science/api/pith-number/7BP75INCHSY3OERSMDLKMAV2GW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7BP75INCHSY3OERSMDLKMAV2GW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7BP75INCHSY3OERSMDLKMAV2GW/action/storage_attestation","attest_author":"https://pith.science/pith/7BP75INCHSY3OERSMDLKMAV2GW/action/author_attestation","sign_citation":"https://pith.science/pith/7BP75INCHSY3OERSMDLKMAV2GW/action/citation_signature","submit_replication":"https://pith.science/pith/7BP75INCHSY3OERSMDLKMAV2GW/action/replication_record"}},"created_at":"2026-07-05T06:40:02.819995+00:00","updated_at":"2026-07-05T06:40:02.819995+00:00"}