{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:RF7GC2BRUOBW2NQ7FGOPHJLK6O","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":"582fb3044341e2a6e2c038de138a2d6a74dd57cd51121eaa9f80149f6bee812d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-03-31T15:09:12Z","title_canon_sha256":"373f728161450def21f010dd90bcd315e1bc857984d0d1861253063dee58b29e"},"schema_version":"1.0","source":{"id":"2203.17090","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2203.17090","created_at":"2026-07-05T04:37:35Z"},{"alias_kind":"arxiv_version","alias_value":"2203.17090v3","created_at":"2026-07-05T04:37:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.17090","created_at":"2026-07-05T04:37:35Z"},{"alias_kind":"pith_short_12","alias_value":"RF7GC2BRUOBW","created_at":"2026-07-05T04:37:35Z"},{"alias_kind":"pith_short_16","alias_value":"RF7GC2BRUOBW2NQ7","created_at":"2026-07-05T04:37:35Z"},{"alias_kind":"pith_short_8","alias_value":"RF7GC2BR","created_at":"2026-07-05T04:37:35Z"}],"graph_snapshots":[{"event_id":"sha256:7b861d10656f375c7359bc537618ea86b5b7423fdbc13c45c7901eaf27e58ca9","target":"graph","created_at":"2026-07-05T04:37:35Z","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/2203.17090/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this paper, we introduce PanGu-Bot, a Chinese pre-trained open-domain dialogue generation model based on a large pre-trained language model (PLM) PANGU-alpha (Zeng et al.,2021). Different from other pre-trained dialogue models trained over a massive amount of dialogue data from scratch, we aim to build a powerful dialogue model with relatively fewer data and computation costs by inheriting valuable language capabilities and knowledge from PLMs. To this end, we train PanGu-Bot from the large PLM PANGU-alpha, which has been proven well-performed on a variety of Chinese natural language tasks.","authors_text":"Chuanfei Xu, Fei Mi, Jingyan Zhou, Lifeng Shang, Qun Liu, Shiqi Zhao, Xin Jiang, Yasheng Wang, Yitong Li, Yulong Zeng","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-03-31T15:09:12Z","title":"PanGu-Bot: Efficient Generative Dialogue Pre-training from Pre-trained Language Model"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.17090","kind":"arxiv","version":3},"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:ae16e111173c030e8665fd2cb370ec280a864e8e1a9b1ab272ae52e578e32782","target":"record","created_at":"2026-07-05T04:37:35Z","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":"582fb3044341e2a6e2c038de138a2d6a74dd57cd51121eaa9f80149f6bee812d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-03-31T15:09:12Z","title_canon_sha256":"373f728161450def21f010dd90bcd315e1bc857984d0d1861253063dee58b29e"},"schema_version":"1.0","source":{"id":"2203.17090","kind":"arxiv","version":3}},"canonical_sha256":"897e616831a3836d361f299cf3a56af39366610dd0241940cf0354db8f3d8c39","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"897e616831a3836d361f299cf3a56af39366610dd0241940cf0354db8f3d8c39","first_computed_at":"2026-07-05T04:37:35.556641Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:37:35.556641Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Wm/8dQ+Dw3saCHL7Ywka3qG4MSBFozrSS0JVibmXlM2es7JH0+zllgzPY5M2qtvYB+3xCsY2x+PeBfc7W8KPAw==","signature_status":"signed_v1","signed_at":"2026-07-05T04:37:35.557215Z","signed_message":"canonical_sha256_bytes"},"source_id":"2203.17090","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ae16e111173c030e8665fd2cb370ec280a864e8e1a9b1ab272ae52e578e32782","sha256:7b861d10656f375c7359bc537618ea86b5b7423fdbc13c45c7901eaf27e58ca9"],"state_sha256":"38ef06e274c739d66b7ddd5b274f9d1feac766aeed5709260b0183fa3254a057"}