{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:AEVFNDB6P2P3GSLKYHF4XYVV5R","short_pith_number":"pith:AEVFNDB6","schema_version":"1.0","canonical_sha256":"012a568c3e7e9fb3496ac1cbcbe2b5ec7481cdfd72e160569929265b5df27020","source":{"kind":"arxiv","id":"2409.17640","version":3},"attestation_state":"computed","paper":{"title":"T3: A Novel Zero-shot Transfer Learning Framework Iteratively Training on an Assistant Task for a Target Task","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Liang Xu, Shijian Fan, Xindi Tong, Yujin Zhu","submitted_at":"2024-09-26T08:44:38Z","abstract_excerpt":"Long text summarization, gradually being essential for efficiently processing large volumes of information, stays challenging for Large Language Models (LLMs) such as GPT and LLaMA families because of the insufficient open-sourced training datasets and the high requirement of contextual details dealing. To address the issue, we design a novel zero-shot transfer learning framework, abbreviated as T3, to iteratively training a baseline LLM on an assistant task for the target task, where the former should own richer data resources and share structural or semantic similarity with the latter. In pr"},"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":"2409.17640","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-09-26T08:44:38Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"dad757132f64ed571be20ead1d0defb78ae716631bc88fd7537b2370228617fb","abstract_canon_sha256":"6aaf99f654686486e2b1cf7f4cbce794064e7c8b41ce4bcb585c87e88344ee24"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:03:46.419146Z","signature_b64":"SMqHzOm9yzJXcUaK/Gir22WyHuJquN9ZZ1/YUH29d5G/uTM+sKIlQiyw5pLOnp+4S1W6RpCOhXi9+ppAutSVBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"012a568c3e7e9fb3496ac1cbcbe2b5ec7481cdfd72e160569929265b5df27020","last_reissued_at":"2026-07-05T10:03:46.418637Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:03:46.418637Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"T3: A Novel Zero-shot Transfer Learning Framework Iteratively Training on an Assistant Task for a Target Task","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Liang Xu, Shijian Fan, Xindi Tong, Yujin Zhu","submitted_at":"2024-09-26T08:44:38Z","abstract_excerpt":"Long text summarization, gradually being essential for efficiently processing large volumes of information, stays challenging for Large Language Models (LLMs) such as GPT and LLaMA families because of the insufficient open-sourced training datasets and the high requirement of contextual details dealing. To address the issue, we design a novel zero-shot transfer learning framework, abbreviated as T3, to iteratively training a baseline LLM on an assistant task for the target task, where the former should own richer data resources and share structural or semantic similarity with the latter. In pr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.17640","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/2409.17640/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":"2409.17640","created_at":"2026-07-05T10:03:46.418695+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.17640v3","created_at":"2026-07-05T10:03:46.418695+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.17640","created_at":"2026-07-05T10:03:46.418695+00:00"},{"alias_kind":"pith_short_12","alias_value":"AEVFNDB6P2P3","created_at":"2026-07-05T10:03:46.418695+00:00"},{"alias_kind":"pith_short_16","alias_value":"AEVFNDB6P2P3GSLK","created_at":"2026-07-05T10:03:46.418695+00:00"},{"alias_kind":"pith_short_8","alias_value":"AEVFNDB6","created_at":"2026-07-05T10:03:46.418695+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/AEVFNDB6P2P3GSLKYHF4XYVV5R","json":"https://pith.science/pith/AEVFNDB6P2P3GSLKYHF4XYVV5R.json","graph_json":"https://pith.science/api/pith-number/AEVFNDB6P2P3GSLKYHF4XYVV5R/graph.json","events_json":"https://pith.science/api/pith-number/AEVFNDB6P2P3GSLKYHF4XYVV5R/events.json","paper":"https://pith.science/paper/AEVFNDB6"},"agent_actions":{"view_html":"https://pith.science/pith/AEVFNDB6P2P3GSLKYHF4XYVV5R","download_json":"https://pith.science/pith/AEVFNDB6P2P3GSLKYHF4XYVV5R.json","view_paper":"https://pith.science/paper/AEVFNDB6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.17640&json=true","fetch_graph":"https://pith.science/api/pith-number/AEVFNDB6P2P3GSLKYHF4XYVV5R/graph.json","fetch_events":"https://pith.science/api/pith-number/AEVFNDB6P2P3GSLKYHF4XYVV5R/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AEVFNDB6P2P3GSLKYHF4XYVV5R/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AEVFNDB6P2P3GSLKYHF4XYVV5R/action/storage_attestation","attest_author":"https://pith.science/pith/AEVFNDB6P2P3GSLKYHF4XYVV5R/action/author_attestation","sign_citation":"https://pith.science/pith/AEVFNDB6P2P3GSLKYHF4XYVV5R/action/citation_signature","submit_replication":"https://pith.science/pith/AEVFNDB6P2P3GSLKYHF4XYVV5R/action/replication_record"}},"created_at":"2026-07-05T10:03:46.418695+00:00","updated_at":"2026-07-05T10:03:46.418695+00:00"}