{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:LRMNOJNELPBNXVC65HWQFLKVNJ","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"2f5826f5316668f2fa0a14f781d71ef732b588b31b3ddb0006c53cd77e1b2a28","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-12-26T03:50:36Z","title_canon_sha256":"054c4581ab70d05a6cd5d4ec0d64e1be3a85a7304f9c542bf40f6582081265cd"},"schema_version":"1.0","source":{"id":"2212.13005","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2212.13005","created_at":"2026-07-05T05:28:15Z"},{"alias_kind":"arxiv_version","alias_value":"2212.13005v1","created_at":"2026-07-05T05:28:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.13005","created_at":"2026-07-05T05:28:15Z"},{"alias_kind":"pith_short_12","alias_value":"LRMNOJNELPBN","created_at":"2026-07-05T05:28:15Z"},{"alias_kind":"pith_short_16","alias_value":"LRMNOJNELPBNXVC6","created_at":"2026-07-05T05:28:15Z"},{"alias_kind":"pith_short_8","alias_value":"LRMNOJNE","created_at":"2026-07-05T05:28:15Z"}],"graph_snapshots":[{"event_id":"sha256:f280f5fb75611d46cc301b7d43edf773863b6f392c83e4bb3ea13b7aba08459a","target":"graph","created_at":"2026-07-05T05:28:15Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2212.13005/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"To facilitate research on text generation, this paper presents a comprehensive and unified library, TextBox 2.0, focusing on the use of pre-trained language models (PLMs). To be comprehensive, our library covers $13$ common text generation tasks and their corresponding $83$ datasets and further incorporates $45$ PLMs covering general, translation, Chinese, dialogue, controllable, distilled, prompting, and lightweight PLMs. We also implement $4$ efficient training strategies and provide $4$ generation objectives for pre-training new PLMs from scratch. To be unified, we design the interfaces to ","authors_text":"Jian-Yun Nie, Ji-Rong Wen, Junyi Li, Tianyi Tang, Wayne Xin Zhao, Wenxun Dai, Xiaoxue Cheng, Yiwen Hu, Yuhao Wang, Zhipeng Chen, Zhuohao Yu, Zican Dong","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-12-26T03:50:36Z","title":"TextBox 2.0: A Text Generation Library with Pre-trained Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.13005","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:b1db26ef4123297943601a38541340d39df3ba7c0d7f1aa07f4a6aa843acbcaf","target":"record","created_at":"2026-07-05T05:28:15Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"2f5826f5316668f2fa0a14f781d71ef732b588b31b3ddb0006c53cd77e1b2a28","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-12-26T03:50:36Z","title_canon_sha256":"054c4581ab70d05a6cd5d4ec0d64e1be3a85a7304f9c542bf40f6582081265cd"},"schema_version":"1.0","source":{"id":"2212.13005","kind":"arxiv","version":1}},"canonical_sha256":"5c58d725a45bc2dbd45ee9ed02ad556a727f2b2b8d737e58359d6f3e6f00df63","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5c58d725a45bc2dbd45ee9ed02ad556a727f2b2b8d737e58359d6f3e6f00df63","first_computed_at":"2026-07-05T05:28:15.641069Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:28:15.641069Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"hx91tycLlVoNSuvxMsjCs7qsf37yyu3FKqohThjgKEK4eiRKvtZc+27d7xZXScv/wLb+/omOwBgEc3ki+VZCCA==","signature_status":"signed_v1","signed_at":"2026-07-05T05:28:15.641433Z","signed_message":"canonical_sha256_bytes"},"source_id":"2212.13005","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b1db26ef4123297943601a38541340d39df3ba7c0d7f1aa07f4a6aa843acbcaf","sha256:f280f5fb75611d46cc301b7d43edf773863b6f392c83e4bb3ea13b7aba08459a"],"state_sha256":"7f57c587d0bd9e8567eef3e787c35c32f06180dcd51180dfe3cd28d49ffac68e"}