{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:RJ3O6UWSWDADIBA4ONYSSIGU3H","short_pith_number":"pith:RJ3O6UWS","schema_version":"1.0","canonical_sha256":"8a76ef52d2b0c034041c73712920d4d9da5cdb241af6e79e317988df79ed66bf","source":{"kind":"arxiv","id":"2411.15477","version":1},"attestation_state":"computed","paper":{"title":"Towards Robust Evaluation of Unlearning in LLMs via Data Transformations","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI","cs.CY","cs.LG"],"primary_cat":"cs.CL","authors_text":"Abhinav Joshi, Ashutosh Modi, Divyaksh Shukla, Harsh Jhamtani, Manas Gaur, Shaswati Saha, Sriram Vema","submitted_at":"2024-11-23T07:20:36Z","abstract_excerpt":"Large Language Models (LLMs) have shown to be a great success in a wide range of applications ranging from regular NLP-based use cases to AI agents. LLMs have been trained on a vast corpus of texts from various sources; despite the best efforts during the data pre-processing stage while training the LLMs, they may pick some undesirable information such as personally identifiable information (PII). Consequently, in recent times research in the area of Machine Unlearning (MUL) has become active, the main idea is to force LLMs to forget (unlearn) certain information (e.g., PII) without suffering "},"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":"2411.15477","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2024-11-23T07:20:36Z","cross_cats_sorted":["cs.AI","cs.CY","cs.LG"],"title_canon_sha256":"7f7742f301c18c8881048450ad1ced8839360f92969be58372f1dca6bb798030","abstract_canon_sha256":"0229f1ce1d708a2e5dc0fded517f69464bb6800a7cdcba1f9b209d6e231faa7e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:40:01.496256Z","signature_b64":"k/4YHqQoWFdtzxHSvKmM3kGr9L7Dgq/eB5FcOAkz+gAube6XHwkfuWe2b8PIB75nxILQZV1UORj6D2KMiSOXAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8a76ef52d2b0c034041c73712920d4d9da5cdb241af6e79e317988df79ed66bf","last_reissued_at":"2026-07-05T09:40:01.495834Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:40:01.495834Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards Robust Evaluation of Unlearning in LLMs via Data Transformations","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI","cs.CY","cs.LG"],"primary_cat":"cs.CL","authors_text":"Abhinav Joshi, Ashutosh Modi, Divyaksh Shukla, Harsh Jhamtani, Manas Gaur, Shaswati Saha, Sriram Vema","submitted_at":"2024-11-23T07:20:36Z","abstract_excerpt":"Large Language Models (LLMs) have shown to be a great success in a wide range of applications ranging from regular NLP-based use cases to AI agents. LLMs have been trained on a vast corpus of texts from various sources; despite the best efforts during the data pre-processing stage while training the LLMs, they may pick some undesirable information such as personally identifiable information (PII). Consequently, in recent times research in the area of Machine Unlearning (MUL) has become active, the main idea is to force LLMs to forget (unlearn) certain information (e.g., PII) without suffering "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.15477","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/2411.15477/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":"2411.15477","created_at":"2026-07-05T09:40:01.495887+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.15477v1","created_at":"2026-07-05T09:40:01.495887+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.15477","created_at":"2026-07-05T09:40:01.495887+00:00"},{"alias_kind":"pith_short_12","alias_value":"RJ3O6UWSWDAD","created_at":"2026-07-05T09:40:01.495887+00:00"},{"alias_kind":"pith_short_16","alias_value":"RJ3O6UWSWDADIBA4","created_at":"2026-07-05T09:40:01.495887+00:00"},{"alias_kind":"pith_short_8","alias_value":"RJ3O6UWS","created_at":"2026-07-05T09:40:01.495887+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RJ3O6UWSWDADIBA4ONYSSIGU3H","json":"https://pith.science/pith/RJ3O6UWSWDADIBA4ONYSSIGU3H.json","graph_json":"https://pith.science/api/pith-number/RJ3O6UWSWDADIBA4ONYSSIGU3H/graph.json","events_json":"https://pith.science/api/pith-number/RJ3O6UWSWDADIBA4ONYSSIGU3H/events.json","paper":"https://pith.science/paper/RJ3O6UWS"},"agent_actions":{"view_html":"https://pith.science/pith/RJ3O6UWSWDADIBA4ONYSSIGU3H","download_json":"https://pith.science/pith/RJ3O6UWSWDADIBA4ONYSSIGU3H.json","view_paper":"https://pith.science/paper/RJ3O6UWS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.15477&json=true","fetch_graph":"https://pith.science/api/pith-number/RJ3O6UWSWDADIBA4ONYSSIGU3H/graph.json","fetch_events":"https://pith.science/api/pith-number/RJ3O6UWSWDADIBA4ONYSSIGU3H/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RJ3O6UWSWDADIBA4ONYSSIGU3H/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RJ3O6UWSWDADIBA4ONYSSIGU3H/action/storage_attestation","attest_author":"https://pith.science/pith/RJ3O6UWSWDADIBA4ONYSSIGU3H/action/author_attestation","sign_citation":"https://pith.science/pith/RJ3O6UWSWDADIBA4ONYSSIGU3H/action/citation_signature","submit_replication":"https://pith.science/pith/RJ3O6UWSWDADIBA4ONYSSIGU3H/action/replication_record"}},"created_at":"2026-07-05T09:40:01.495887+00:00","updated_at":"2026-07-05T09:40:01.495887+00:00"}