{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:LBRLRQ67VONLOTHMFZF35IVPIZ","short_pith_number":"pith:LBRLRQ67","schema_version":"1.0","canonical_sha256":"5862b8c3dfab9ab74cec2e4bbea2af46557fa76586791f8b12fe4bbaf6692c31","source":{"kind":"arxiv","id":"2201.05273","version":4},"attestation_state":"computed","paper":{"title":"Pretrained Language Models for Text Generation: A Survey","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Jian-Yun Nie, Ji-Rong Wen, Junyi Li, Tianyi Tang, Wayne Xin Zhao","submitted_at":"2022-01-14T01:44:58Z","abstract_excerpt":"Text Generation aims to produce plausible and readable text in a human language from input data. The resurgence of deep learning has greatly advanced this field, in particular, with the help of neural generation models based on pre-trained language models (PLMs). Text generation based on PLMs is viewed as a promising approach in both academia and industry. In this paper, we provide a survey on the utilization of PLMs in text generation. We begin with introducing three key aspects of applying PLMs to text generation: 1) how to encode the input into representations preserving input semantics whi"},"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":"2201.05273","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-01-14T01:44:58Z","cross_cats_sorted":[],"title_canon_sha256":"3b8a27d8054384ac5619bd5b8386cfa33f287eef906f8be2fb8a202f3056db2c","abstract_canon_sha256":"72bfb3ab6c471aa29de694ff0d34fb4ce0204dfd48270debf017289bbf8bcdc0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:23:06.812979Z","signature_b64":"6mdUyvCcNhuvrxjZ6Iowz9cwAO6d+2RTf49wWCR6EGMHLOQW8v9AbJLrIMfVux2LzseND8LyYIwRiRSIPugeAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5862b8c3dfab9ab74cec2e4bbea2af46557fa76586791f8b12fe4bbaf6692c31","last_reissued_at":"2026-07-05T04:23:06.812514Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:23:06.812514Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Pretrained Language Models for Text Generation: A Survey","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Jian-Yun Nie, Ji-Rong Wen, Junyi Li, Tianyi Tang, Wayne Xin Zhao","submitted_at":"2022-01-14T01:44:58Z","abstract_excerpt":"Text Generation aims to produce plausible and readable text in a human language from input data. The resurgence of deep learning has greatly advanced this field, in particular, with the help of neural generation models based on pre-trained language models (PLMs). Text generation based on PLMs is viewed as a promising approach in both academia and industry. In this paper, we provide a survey on the utilization of PLMs in text generation. We begin with introducing three key aspects of applying PLMs to text generation: 1) how to encode the input into representations preserving input semantics whi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2201.05273","kind":"arxiv","version":4},"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/2201.05273/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":"2201.05273","created_at":"2026-07-05T04:23:06.812567+00:00"},{"alias_kind":"arxiv_version","alias_value":"2201.05273v4","created_at":"2026-07-05T04:23:06.812567+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2201.05273","created_at":"2026-07-05T04:23:06.812567+00:00"},{"alias_kind":"pith_short_12","alias_value":"LBRLRQ67VONL","created_at":"2026-07-05T04:23:06.812567+00:00"},{"alias_kind":"pith_short_16","alias_value":"LBRLRQ67VONLOTHM","created_at":"2026-07-05T04:23:06.812567+00:00"},{"alias_kind":"pith_short_8","alias_value":"LBRLRQ67","created_at":"2026-07-05T04:23:06.812567+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.17767","citing_title":"ISACL: Internal State Analyzer for Copyrighted Training Data Leakage","ref_index":29,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LBRLRQ67VONLOTHMFZF35IVPIZ","json":"https://pith.science/pith/LBRLRQ67VONLOTHMFZF35IVPIZ.json","graph_json":"https://pith.science/api/pith-number/LBRLRQ67VONLOTHMFZF35IVPIZ/graph.json","events_json":"https://pith.science/api/pith-number/LBRLRQ67VONLOTHMFZF35IVPIZ/events.json","paper":"https://pith.science/paper/LBRLRQ67"},"agent_actions":{"view_html":"https://pith.science/pith/LBRLRQ67VONLOTHMFZF35IVPIZ","download_json":"https://pith.science/pith/LBRLRQ67VONLOTHMFZF35IVPIZ.json","view_paper":"https://pith.science/paper/LBRLRQ67","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2201.05273&json=true","fetch_graph":"https://pith.science/api/pith-number/LBRLRQ67VONLOTHMFZF35IVPIZ/graph.json","fetch_events":"https://pith.science/api/pith-number/LBRLRQ67VONLOTHMFZF35IVPIZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LBRLRQ67VONLOTHMFZF35IVPIZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LBRLRQ67VONLOTHMFZF35IVPIZ/action/storage_attestation","attest_author":"https://pith.science/pith/LBRLRQ67VONLOTHMFZF35IVPIZ/action/author_attestation","sign_citation":"https://pith.science/pith/LBRLRQ67VONLOTHMFZF35IVPIZ/action/citation_signature","submit_replication":"https://pith.science/pith/LBRLRQ67VONLOTHMFZF35IVPIZ/action/replication_record"}},"created_at":"2026-07-05T04:23:06.812567+00:00","updated_at":"2026-07-05T04:23:06.812567+00:00"}