{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:4KEOMMGLRBFJMF2KEKAYCDH7UF","short_pith_number":"pith:4KEOMMGL","schema_version":"1.0","canonical_sha256":"e288e630cb884a96174a2281810cffa16f315eae733b49d09a254ca1bdef8794","source":{"kind":"arxiv","id":"2305.15275","version":1},"attestation_state":"computed","paper":{"title":"Self-Evolution Learning for Discriminative Language Model Pretraining","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Bo Du, Dacheng Tao, Juhua Liu, Liang Ding, Qihuang Zhong","submitted_at":"2023-05-24T16:00:54Z","abstract_excerpt":"Masked language modeling, widely used in discriminative language model (e.g., BERT) pretraining, commonly adopts a random masking strategy. However, random masking does not consider the importance of the different words in the sentence meaning, where some of them are more worthy to be predicted. Therefore, various masking strategies (e.g., entity-level masking) are proposed, but most of them require expensive prior knowledge and generally train from scratch without reusing existing model weights. In this paper, we present Self-Evolution learning (SE), a simple and effective token masking and l"},"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":"2305.15275","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-05-24T16:00:54Z","cross_cats_sorted":[],"title_canon_sha256":"4300692a3647114b30caac08042293dc384afd9b8b1a5a3235f8654293451f80","abstract_canon_sha256":"5f5b570c49771495b8911870efee8a7427bae78e8d222f8c0c4e083ff0180d17"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:13:33.245297Z","signature_b64":"C6dZo+pTZDuNtmRlFu+bbv8VPImuEr3UvHZringZaKrmm3ImRJbk9u37p5ADoZWcAG8JrVKuhA8h79Ag/TJNDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e288e630cb884a96174a2281810cffa16f315eae733b49d09a254ca1bdef8794","last_reissued_at":"2026-07-05T06:13:33.244755Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:13:33.244755Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Self-Evolution Learning for Discriminative Language Model Pretraining","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Bo Du, Dacheng Tao, Juhua Liu, Liang Ding, Qihuang Zhong","submitted_at":"2023-05-24T16:00:54Z","abstract_excerpt":"Masked language modeling, widely used in discriminative language model (e.g., BERT) pretraining, commonly adopts a random masking strategy. However, random masking does not consider the importance of the different words in the sentence meaning, where some of them are more worthy to be predicted. Therefore, various masking strategies (e.g., entity-level masking) are proposed, but most of them require expensive prior knowledge and generally train from scratch without reusing existing model weights. In this paper, we present Self-Evolution learning (SE), a simple and effective token masking and l"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.15275","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/2305.15275/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":"2305.15275","created_at":"2026-07-05T06:13:33.244811+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.15275v1","created_at":"2026-07-05T06:13:33.244811+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.15275","created_at":"2026-07-05T06:13:33.244811+00:00"},{"alias_kind":"pith_short_12","alias_value":"4KEOMMGLRBFJ","created_at":"2026-07-05T06:13:33.244811+00:00"},{"alias_kind":"pith_short_16","alias_value":"4KEOMMGLRBFJMF2K","created_at":"2026-07-05T06:13:33.244811+00:00"},{"alias_kind":"pith_short_8","alias_value":"4KEOMMGL","created_at":"2026-07-05T06:13:33.244811+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.15303","citing_title":"Self-Evolution Knowledge Distillation for LLM-based Machine Translation","ref_index":51,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4KEOMMGLRBFJMF2KEKAYCDH7UF","json":"https://pith.science/pith/4KEOMMGLRBFJMF2KEKAYCDH7UF.json","graph_json":"https://pith.science/api/pith-number/4KEOMMGLRBFJMF2KEKAYCDH7UF/graph.json","events_json":"https://pith.science/api/pith-number/4KEOMMGLRBFJMF2KEKAYCDH7UF/events.json","paper":"https://pith.science/paper/4KEOMMGL"},"agent_actions":{"view_html":"https://pith.science/pith/4KEOMMGLRBFJMF2KEKAYCDH7UF","download_json":"https://pith.science/pith/4KEOMMGLRBFJMF2KEKAYCDH7UF.json","view_paper":"https://pith.science/paper/4KEOMMGL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.15275&json=true","fetch_graph":"https://pith.science/api/pith-number/4KEOMMGLRBFJMF2KEKAYCDH7UF/graph.json","fetch_events":"https://pith.science/api/pith-number/4KEOMMGLRBFJMF2KEKAYCDH7UF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4KEOMMGLRBFJMF2KEKAYCDH7UF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4KEOMMGLRBFJMF2KEKAYCDH7UF/action/storage_attestation","attest_author":"https://pith.science/pith/4KEOMMGLRBFJMF2KEKAYCDH7UF/action/author_attestation","sign_citation":"https://pith.science/pith/4KEOMMGLRBFJMF2KEKAYCDH7UF/action/citation_signature","submit_replication":"https://pith.science/pith/4KEOMMGLRBFJMF2KEKAYCDH7UF/action/replication_record"}},"created_at":"2026-07-05T06:13:33.244811+00:00","updated_at":"2026-07-05T06:13:33.244811+00:00"}