{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:UF6WAYVVY3SIXRONEL4AEBQSCD","short_pith_number":"pith:UF6WAYVV","canonical_record":{"source":{"id":"2202.13257","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2022-02-27T00:31:03Z","cross_cats_sorted":[],"title_canon_sha256":"e12f4c4bae427655d9569793bcb51b218c41989ec56beabbc734b39ec52bb0eb","abstract_canon_sha256":"47182f0cafdf97a08391907723bae66045a5238898a873fcf7c6fdf820d9b54f"},"schema_version":"1.0"},"canonical_sha256":"a17d6062b5c6e48bc5cd22f802061210e402d3a53f2f2ea9580835fba1a7909e","source":{"kind":"arxiv","id":"2202.13257","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2202.13257","created_at":"2026-07-05T04:00:27Z"},{"alias_kind":"arxiv_version","alias_value":"2202.13257v1","created_at":"2026-07-05T04:00:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.13257","created_at":"2026-07-05T04:00:27Z"},{"alias_kind":"pith_short_12","alias_value":"UF6WAYVVY3SI","created_at":"2026-07-05T04:00:27Z"},{"alias_kind":"pith_short_16","alias_value":"UF6WAYVVY3SIXRON","created_at":"2026-07-05T04:00:27Z"},{"alias_kind":"pith_short_8","alias_value":"UF6WAYVV","created_at":"2026-07-05T04:00:27Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:UF6WAYVVY3SIXRONEL4AEBQSCD","target":"record","payload":{"canonical_record":{"source":{"id":"2202.13257","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2022-02-27T00:31:03Z","cross_cats_sorted":[],"title_canon_sha256":"e12f4c4bae427655d9569793bcb51b218c41989ec56beabbc734b39ec52bb0eb","abstract_canon_sha256":"47182f0cafdf97a08391907723bae66045a5238898a873fcf7c6fdf820d9b54f"},"schema_version":"1.0"},"canonical_sha256":"a17d6062b5c6e48bc5cd22f802061210e402d3a53f2f2ea9580835fba1a7909e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:00:27.848022Z","signature_b64":"EGPWfHwCcJIntRkgC4fmu/5vQRBovSuZ4pMlN7kidIDvTbQ0/Pzc1VgOXSjRSqpDrfqlE3o3TLR7HXgC9uFADQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a17d6062b5c6e48bc5cd22f802061210e402d3a53f2f2ea9580835fba1a7909e","last_reissued_at":"2026-07-05T04:00:27.847497Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:00:27.847497Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2202.13257","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:00:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"m85FGS3ywVIK3e4hOPHtvGssl/tNRq8y3WczUTfGVGTKlaNrPQ9WApjWZbbKMyKmKh6/TUaDStbQ5nrFm2poBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T09:20:09.507916Z"},"content_sha256":"6eed8a76d348a1d389e9c36bab593e351b3baf9f92b5e1c6ba9524d30971620c","schema_version":"1.0","event_id":"sha256:6eed8a76d348a1d389e9c36bab593e351b3baf9f92b5e1c6ba9524d30971620c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:UF6WAYVVY3SIXRONEL4AEBQSCD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Controllable Natural Language Generation with Contrastive Prefixes","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Furu Wei, Jing Qian, Li Dong, Weizhu Chen, Yelong Shen","submitted_at":"2022-02-27T00:31:03Z","abstract_excerpt":"To guide the generation of large pretrained language models (LM), previous work has focused on directly fine-tuning the language model or utilizing an attribute discriminator. In this work, we propose a novel lightweight framework for controllable GPT2 generation, which utilizes a set of small attribute-specific vectors, called prefixes, to steer natural language generation. Different from prefix-tuning, where each prefix is trained independently, we take the relationship among prefixes into consideration and train multiple prefixes simultaneously. We propose a novel supervised method and also"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.13257","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/2202.13257/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:00:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"y+v/JIpEFZk+/pOfrSXgQpxW5sHMkdYYov/eusP9OwtGart8P7fKHkxBNtpEmiNl43secy2yQMrJZ04koJD4Dw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T09:20:09.508421Z"},"content_sha256":"4f26b45329e8944e83c2e1f19308b6af13fd4591801265cc8d3ab0060de9caca","schema_version":"1.0","event_id":"sha256:4f26b45329e8944e83c2e1f19308b6af13fd4591801265cc8d3ab0060de9caca"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UF6WAYVVY3SIXRONEL4AEBQSCD/bundle.json","state_url":"https://pith.science/pith/UF6WAYVVY3SIXRONEL4AEBQSCD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UF6WAYVVY3SIXRONEL4AEBQSCD/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-08T09:20:09Z","links":{"resolver":"https://pith.science/pith/UF6WAYVVY3SIXRONEL4AEBQSCD","bundle":"https://pith.science/pith/UF6WAYVVY3SIXRONEL4AEBQSCD/bundle.json","state":"https://pith.science/pith/UF6WAYVVY3SIXRONEL4AEBQSCD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UF6WAYVVY3SIXRONEL4AEBQSCD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:UF6WAYVVY3SIXRONEL4AEBQSCD","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":"47182f0cafdf97a08391907723bae66045a5238898a873fcf7c6fdf820d9b54f","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2022-02-27T00:31:03Z","title_canon_sha256":"e12f4c4bae427655d9569793bcb51b218c41989ec56beabbc734b39ec52bb0eb"},"schema_version":"1.0","source":{"id":"2202.13257","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2202.13257","created_at":"2026-07-05T04:00:27Z"},{"alias_kind":"arxiv_version","alias_value":"2202.13257v1","created_at":"2026-07-05T04:00:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.13257","created_at":"2026-07-05T04:00:27Z"},{"alias_kind":"pith_short_12","alias_value":"UF6WAYVVY3SI","created_at":"2026-07-05T04:00:27Z"},{"alias_kind":"pith_short_16","alias_value":"UF6WAYVVY3SIXRON","created_at":"2026-07-05T04:00:27Z"},{"alias_kind":"pith_short_8","alias_value":"UF6WAYVV","created_at":"2026-07-05T04:00:27Z"}],"graph_snapshots":[{"event_id":"sha256:4f26b45329e8944e83c2e1f19308b6af13fd4591801265cc8d3ab0060de9caca","target":"graph","created_at":"2026-07-05T04:00:27Z","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/2202.13257/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"To guide the generation of large pretrained language models (LM), previous work has focused on directly fine-tuning the language model or utilizing an attribute discriminator. In this work, we propose a novel lightweight framework for controllable GPT2 generation, which utilizes a set of small attribute-specific vectors, called prefixes, to steer natural language generation. Different from prefix-tuning, where each prefix is trained independently, we take the relationship among prefixes into consideration and train multiple prefixes simultaneously. We propose a novel supervised method and also","authors_text":"Furu Wei, Jing Qian, Li Dong, Weizhu Chen, Yelong Shen","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2022-02-27T00:31:03Z","title":"Controllable Natural Language Generation with Contrastive Prefixes"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.13257","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":"sha256:6eed8a76d348a1d389e9c36bab593e351b3baf9f92b5e1c6ba9524d30971620c","target":"record","created_at":"2026-07-05T04:00:27Z","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":"47182f0cafdf97a08391907723bae66045a5238898a873fcf7c6fdf820d9b54f","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2022-02-27T00:31:03Z","title_canon_sha256":"e12f4c4bae427655d9569793bcb51b218c41989ec56beabbc734b39ec52bb0eb"},"schema_version":"1.0","source":{"id":"2202.13257","kind":"arxiv","version":1}},"canonical_sha256":"a17d6062b5c6e48bc5cd22f802061210e402d3a53f2f2ea9580835fba1a7909e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a17d6062b5c6e48bc5cd22f802061210e402d3a53f2f2ea9580835fba1a7909e","first_computed_at":"2026-07-05T04:00:27.847497Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:00:27.847497Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"EGPWfHwCcJIntRkgC4fmu/5vQRBovSuZ4pMlN7kidIDvTbQ0/Pzc1VgOXSjRSqpDrfqlE3o3TLR7HXgC9uFADQ==","signature_status":"signed_v1","signed_at":"2026-07-05T04:00:27.848022Z","signed_message":"canonical_sha256_bytes"},"source_id":"2202.13257","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6eed8a76d348a1d389e9c36bab593e351b3baf9f92b5e1c6ba9524d30971620c","sha256:4f26b45329e8944e83c2e1f19308b6af13fd4591801265cc8d3ab0060de9caca"],"state_sha256":"18560f112b99e81b19a91d8f0a21f07ebfc99b23e6af8cf12fb2308aca405855"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+BmVbVzx7kbfV7AEgUxkFSQCvKpCnLHlZpqpHxwr0srVSHoI3yHtTPCPmCxtDP8V9onUnb8nzTxvd+xbWJHcAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T09:20:09.513189Z","bundle_sha256":"5c48ca471896461f583e3f943c711371876c1c84824addc5fd09e65d2b723e67"}}