{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:CPGL7K7W4O55I2SMUS2JENXASV","short_pith_number":"pith:CPGL7K7W","schema_version":"1.0","canonical_sha256":"13ccbfabf6e3bbd46a4ca4b49236e09548fcec622c930d2e61b752488e412429","source":{"kind":"arxiv","id":"2210.17546","version":3},"attestation_state":"computed","paper":{"title":"Preventing Verbatim Memorization in Language Models Gives a False Sense of Privacy","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.LG","authors_text":"Chiyuan Zhang, Christopher A. Choquette-Choo, Daphne Ippolito, Florian Tram\\`er, Katherine Lee, Matthew Jagielski, Milad Nasr, Nicholas Carlini","submitted_at":"2022-10-31T17:57:55Z","abstract_excerpt":"Studying data memorization in neural language models helps us understand the risks (e.g., to privacy or copyright) associated with models regurgitating training data and aids in the development of countermeasures. Many prior works -- and some recently deployed defenses -- focus on \"verbatim memorization\", defined as a model generation that exactly matches a substring from the training set. We argue that verbatim memorization definitions are too restrictive and fail to capture more subtle forms of memorization. Specifically, we design and implement an efficient defense that perfectly prevents a"},"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":"2210.17546","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-10-31T17:57:55Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"ae7a770484e71c93fe89311e39d8bbef628e1efc109f847914433751f35372e6","abstract_canon_sha256":"03fc711715c0244f6ef3f815b228b49b49c74af0a688988ddae5381efd13f31e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:49:17.175404Z","signature_b64":"G1VPwEtYw2eXtF6dJMseEPktPE+bFsC7EXBd9PBA6qLDMBHvbU41eiVo2R3fZKjNVomwPSxACf/xvOxM1WrnDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"13ccbfabf6e3bbd46a4ca4b49236e09548fcec622c930d2e61b752488e412429","last_reissued_at":"2026-07-05T06:49:17.174894Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:49:17.174894Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Preventing Verbatim Memorization in Language Models Gives a False Sense of Privacy","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.LG","authors_text":"Chiyuan Zhang, Christopher A. Choquette-Choo, Daphne Ippolito, Florian Tram\\`er, Katherine Lee, Matthew Jagielski, Milad Nasr, Nicholas Carlini","submitted_at":"2022-10-31T17:57:55Z","abstract_excerpt":"Studying data memorization in neural language models helps us understand the risks (e.g., to privacy or copyright) associated with models regurgitating training data and aids in the development of countermeasures. Many prior works -- and some recently deployed defenses -- focus on \"verbatim memorization\", defined as a model generation that exactly matches a substring from the training set. We argue that verbatim memorization definitions are too restrictive and fail to capture more subtle forms of memorization. Specifically, we design and implement an efficient defense that perfectly prevents a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.17546","kind":"arxiv","version":3},"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/2210.17546/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":"2210.17546","created_at":"2026-07-05T06:49:17.174946+00:00"},{"alias_kind":"arxiv_version","alias_value":"2210.17546v3","created_at":"2026-07-05T06:49:17.174946+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.17546","created_at":"2026-07-05T06:49:17.174946+00:00"},{"alias_kind":"pith_short_12","alias_value":"CPGL7K7W4O55","created_at":"2026-07-05T06:49:17.174946+00:00"},{"alias_kind":"pith_short_16","alias_value":"CPGL7K7W4O55I2SM","created_at":"2026-07-05T06:49:17.174946+00:00"},{"alias_kind":"pith_short_8","alias_value":"CPGL7K7W","created_at":"2026-07-05T06:49:17.174946+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":9,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.09125","citing_title":"Unveiling Privacy Risks in Multi-modal Large Language Models: Task-specific Vulnerabilities and Mitigation Challenges","ref_index":12,"is_internal_anchor":false},{"citing_arxiv_id":"2503.19786","citing_title":"Gemma 3 Technical Report","ref_index":25,"is_internal_anchor":false},{"citing_arxiv_id":"2605.15248","citing_title":"Probing Privacy Leaks in LLM-based Code Generation via Test Generation","ref_index":40,"is_internal_anchor":false},{"citing_arxiv_id":"2507.06261","citing_title":"Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities","ref_index":35,"is_internal_anchor":false},{"citing_arxiv_id":"2304.01373","citing_title":"Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling","ref_index":202,"is_internal_anchor":false},{"citing_arxiv_id":"2305.10403","citing_title":"PaLM 2 Technical Report","ref_index":171,"is_internal_anchor":false},{"citing_arxiv_id":"2605.03547","citing_title":"Erase Persona, Forget Lore: Benchmarking Multimodal Copyright Unlearning in Large Vision Language Models","ref_index":43,"is_internal_anchor":false},{"citing_arxiv_id":"2403.08295","citing_title":"Gemma: Open Models Based on Gemini Research and Technology","ref_index":61,"is_internal_anchor":false},{"citing_arxiv_id":"2408.00118","citing_title":"Gemma 2: Improving Open Language Models at a Practical Size","ref_index":64,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CPGL7K7W4O55I2SMUS2JENXASV","json":"https://pith.science/pith/CPGL7K7W4O55I2SMUS2JENXASV.json","graph_json":"https://pith.science/api/pith-number/CPGL7K7W4O55I2SMUS2JENXASV/graph.json","events_json":"https://pith.science/api/pith-number/CPGL7K7W4O55I2SMUS2JENXASV/events.json","paper":"https://pith.science/paper/CPGL7K7W"},"agent_actions":{"view_html":"https://pith.science/pith/CPGL7K7W4O55I2SMUS2JENXASV","download_json":"https://pith.science/pith/CPGL7K7W4O55I2SMUS2JENXASV.json","view_paper":"https://pith.science/paper/CPGL7K7W","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2210.17546&json=true","fetch_graph":"https://pith.science/api/pith-number/CPGL7K7W4O55I2SMUS2JENXASV/graph.json","fetch_events":"https://pith.science/api/pith-number/CPGL7K7W4O55I2SMUS2JENXASV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CPGL7K7W4O55I2SMUS2JENXASV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CPGL7K7W4O55I2SMUS2JENXASV/action/storage_attestation","attest_author":"https://pith.science/pith/CPGL7K7W4O55I2SMUS2JENXASV/action/author_attestation","sign_citation":"https://pith.science/pith/CPGL7K7W4O55I2SMUS2JENXASV/action/citation_signature","submit_replication":"https://pith.science/pith/CPGL7K7W4O55I2SMUS2JENXASV/action/replication_record"}},"created_at":"2026-07-05T06:49:17.174946+00:00","updated_at":"2026-07-05T06:49:17.174946+00:00"}