{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:NTST334FSLRJSYRJZDJSTJF2IZ","short_pith_number":"pith:NTST334F","schema_version":"1.0","canonical_sha256":"6ce53def8592e2996229c8d329a4ba464832ed190e076d97a06950822fa4b634","source":{"kind":"arxiv","id":"2412.12541","version":1},"attestation_state":"computed","paper":{"title":"LLMCL-GEC: Advancing Grammatical Error Correction with LLM-Driven Curriculum Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Derek F. Wong, Jinlong Hou, Keyan Jin, Lidia S. Chao, Lusheng Zhang, Qiang Zhang, Tao Fang, Tianjiao Li","submitted_at":"2024-12-17T05:09:07Z","abstract_excerpt":"While large-scale language models (LLMs) have demonstrated remarkable capabilities in specific natural language processing (NLP) tasks, they may still lack proficiency compared to specialized models in certain domains, such as grammatical error correction (GEC). Drawing inspiration from the concept of curriculum learning, we have delved into refining LLMs into proficient GEC experts by devising effective curriculum learning (CL) strategies. In this paper, we introduce a novel approach, termed LLM-based curriculum learning, which capitalizes on the robust semantic comprehension and discriminati"},"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":"2412.12541","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-12-17T05:09:07Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"0cb7feb87575105860641d3e38f2abe9ff8d28a670d7ba27bba39d5f6ba5cfbe","abstract_canon_sha256":"be9ef307b7198df591ac9f5bbf8bd0c35bee469eeb23d85c5c10b5a68bd9882a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:50:16.646869Z","signature_b64":"quWhmEWrbKxv1SF6zBKIhUHN+BuC+u5xJlsjpoOxpEEgxewJnMRmDrZBJF2fOJXQYcm5aIGTrlw9qBgSWi1pAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6ce53def8592e2996229c8d329a4ba464832ed190e076d97a06950822fa4b634","last_reissued_at":"2026-07-05T09:50:16.646388Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:50:16.646388Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LLMCL-GEC: Advancing Grammatical Error Correction with LLM-Driven Curriculum Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Derek F. Wong, Jinlong Hou, Keyan Jin, Lidia S. Chao, Lusheng Zhang, Qiang Zhang, Tao Fang, Tianjiao Li","submitted_at":"2024-12-17T05:09:07Z","abstract_excerpt":"While large-scale language models (LLMs) have demonstrated remarkable capabilities in specific natural language processing (NLP) tasks, they may still lack proficiency compared to specialized models in certain domains, such as grammatical error correction (GEC). Drawing inspiration from the concept of curriculum learning, we have delved into refining LLMs into proficient GEC experts by devising effective curriculum learning (CL) strategies. In this paper, we introduce a novel approach, termed LLM-based curriculum learning, which capitalizes on the robust semantic comprehension and discriminati"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.12541","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/2412.12541/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":"2412.12541","created_at":"2026-07-05T09:50:16.646451+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.12541v1","created_at":"2026-07-05T09:50:16.646451+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.12541","created_at":"2026-07-05T09:50:16.646451+00:00"},{"alias_kind":"pith_short_12","alias_value":"NTST334FSLRJ","created_at":"2026-07-05T09:50:16.646451+00:00"},{"alias_kind":"pith_short_16","alias_value":"NTST334FSLRJSYRJ","created_at":"2026-07-05T09:50:16.646451+00:00"},{"alias_kind":"pith_short_8","alias_value":"NTST334F","created_at":"2026-07-05T09:50:16.646451+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.07996","citing_title":"MC-PDD: Masked Corpus-Level Pretraining Data Detection for Black-Box Large Language Models","ref_index":2,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NTST334FSLRJSYRJZDJSTJF2IZ","json":"https://pith.science/pith/NTST334FSLRJSYRJZDJSTJF2IZ.json","graph_json":"https://pith.science/api/pith-number/NTST334FSLRJSYRJZDJSTJF2IZ/graph.json","events_json":"https://pith.science/api/pith-number/NTST334FSLRJSYRJZDJSTJF2IZ/events.json","paper":"https://pith.science/paper/NTST334F"},"agent_actions":{"view_html":"https://pith.science/pith/NTST334FSLRJSYRJZDJSTJF2IZ","download_json":"https://pith.science/pith/NTST334FSLRJSYRJZDJSTJF2IZ.json","view_paper":"https://pith.science/paper/NTST334F","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.12541&json=true","fetch_graph":"https://pith.science/api/pith-number/NTST334FSLRJSYRJZDJSTJF2IZ/graph.json","fetch_events":"https://pith.science/api/pith-number/NTST334FSLRJSYRJZDJSTJF2IZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NTST334FSLRJSYRJZDJSTJF2IZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NTST334FSLRJSYRJZDJSTJF2IZ/action/storage_attestation","attest_author":"https://pith.science/pith/NTST334FSLRJSYRJZDJSTJF2IZ/action/author_attestation","sign_citation":"https://pith.science/pith/NTST334FSLRJSYRJZDJSTJF2IZ/action/citation_signature","submit_replication":"https://pith.science/pith/NTST334FSLRJSYRJZDJSTJF2IZ/action/replication_record"}},"created_at":"2026-07-05T09:50:16.646451+00:00","updated_at":"2026-07-05T09:50:16.646451+00:00"}