{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:437CQS27UKPFZBP6CL5EN5NAXT","short_pith_number":"pith:437CQS27","canonical_record":{"source":{"id":"2108.13487","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-08-30T19:18:24Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"47cdeb28952efc8f094f3c301d6a2a877892d1b60c1e5fa0347e2a2966725b48","abstract_canon_sha256":"473dbefe78a3359a326f11d846c322e81c701acd2a0e61cbe53fe03cd3e8c588"},"schema_version":"1.0"},"canonical_sha256":"e6fe284b5fa29e5c85fe12fa46f5a0bcd78d5f6c2daa5dbff00dca8af435b8d6","source":{"kind":"arxiv","id":"2108.13487","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2108.13487","created_at":"2026-07-05T03:10:14Z"},{"alias_kind":"arxiv_version","alias_value":"2108.13487v1","created_at":"2026-07-05T03:10:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2108.13487","created_at":"2026-07-05T03:10:14Z"},{"alias_kind":"pith_short_12","alias_value":"437CQS27UKPF","created_at":"2026-07-05T03:10:14Z"},{"alias_kind":"pith_short_16","alias_value":"437CQS27UKPFZBP6","created_at":"2026-07-05T03:10:14Z"},{"alias_kind":"pith_short_8","alias_value":"437CQS27","created_at":"2026-07-05T03:10:14Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:437CQS27UKPFZBP6CL5EN5NAXT","target":"record","payload":{"canonical_record":{"source":{"id":"2108.13487","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-08-30T19:18:24Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"47cdeb28952efc8f094f3c301d6a2a877892d1b60c1e5fa0347e2a2966725b48","abstract_canon_sha256":"473dbefe78a3359a326f11d846c322e81c701acd2a0e61cbe53fe03cd3e8c588"},"schema_version":"1.0"},"canonical_sha256":"e6fe284b5fa29e5c85fe12fa46f5a0bcd78d5f6c2daa5dbff00dca8af435b8d6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:10:14.687572Z","signature_b64":"Kak8+6vOG0fp9OJcaI1EUvX01DViqF1Ea/YLRFl6rhNsvNp6xHH1Gfe2XWJTMexPPlJ5bYToZootYM5+TsjEDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e6fe284b5fa29e5c85fe12fa46f5a0bcd78d5f6c2daa5dbff00dca8af435b8d6","last_reissued_at":"2026-07-05T03:10:14.687070Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:10:14.687070Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2108.13487","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":"2026-07-05T03:10:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IxtirwuhBT5cP593tmXBR0rje4EjEFey6WPOF/RTtrWJdwrjgyWTtTNBU6+58bjxc0dhdBzTQE00gm3bYPgFAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T17:20:00.880600Z"},"content_sha256":"dcba12e91fd585872917c90e31cb6e717d157e7a3df36ef93ed1fb1fa79c7831","schema_version":"1.0","event_id":"sha256:dcba12e91fd585872917c90e31cb6e717d157e7a3df36ef93ed1fb1fa79c7831"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:437CQS27UKPFZBP6CL5EN5NAXT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Want To Reduce Labeling Cost? GPT-3 Can Help","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Chenguang Zhu, Michael Zeng, Shuohang Wang, Yang Liu, Yichong Xu","submitted_at":"2021-08-30T19:18:24Z","abstract_excerpt":"Data annotation is a time-consuming and labor-intensive process for many NLP tasks. Although there exist various methods to produce pseudo data labels, they are often task-specific and require a decent amount of labeled data to start with. Recently, the immense language model GPT-3 with 175 billion parameters has achieved tremendous improvement across many few-shot learning tasks. In this paper, we explore ways to leverage GPT-3 as a low-cost data labeler to train other models. We find that, to make the downstream model achieve the same performance on a variety of NLU and NLG tasks, it costs 5"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2108.13487","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/2108.13487/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":"2026-07-05T03:10:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Dp7NbapMdCYcwgPOcGMji3az02t9ofYD2vbjOZBx/8txg6ppIbec4eavevqshjEbNWh9jjT/Lo89/p78JiMWCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T17:20:00.881438Z"},"content_sha256":"ec94a272fc184d34c35e8123ba552837c2fe3f5425f1c83f8c6f1e5518baf916","schema_version":"1.0","event_id":"sha256:ec94a272fc184d34c35e8123ba552837c2fe3f5425f1c83f8c6f1e5518baf916"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/437CQS27UKPFZBP6CL5EN5NAXT/bundle.json","state_url":"https://pith.science/pith/437CQS27UKPFZBP6CL5EN5NAXT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/437CQS27UKPFZBP6CL5EN5NAXT/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":"2026-08-05T17:20:00Z","links":{"resolver":"https://pith.science/pith/437CQS27UKPFZBP6CL5EN5NAXT","bundle":"https://pith.science/pith/437CQS27UKPFZBP6CL5EN5NAXT/bundle.json","state":"https://pith.science/pith/437CQS27UKPFZBP6CL5EN5NAXT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/437CQS27UKPFZBP6CL5EN5NAXT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:437CQS27UKPFZBP6CL5EN5NAXT","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":"473dbefe78a3359a326f11d846c322e81c701acd2a0e61cbe53fe03cd3e8c588","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-08-30T19:18:24Z","title_canon_sha256":"47cdeb28952efc8f094f3c301d6a2a877892d1b60c1e5fa0347e2a2966725b48"},"schema_version":"1.0","source":{"id":"2108.13487","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2108.13487","created_at":"2026-07-05T03:10:14Z"},{"alias_kind":"arxiv_version","alias_value":"2108.13487v1","created_at":"2026-07-05T03:10:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2108.13487","created_at":"2026-07-05T03:10:14Z"},{"alias_kind":"pith_short_12","alias_value":"437CQS27UKPF","created_at":"2026-07-05T03:10:14Z"},{"alias_kind":"pith_short_16","alias_value":"437CQS27UKPFZBP6","created_at":"2026-07-05T03:10:14Z"},{"alias_kind":"pith_short_8","alias_value":"437CQS27","created_at":"2026-07-05T03:10:14Z"}],"graph_snapshots":[{"event_id":"sha256:ec94a272fc184d34c35e8123ba552837c2fe3f5425f1c83f8c6f1e5518baf916","target":"graph","created_at":"2026-07-05T03:10:14Z","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/2108.13487/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Data annotation is a time-consuming and labor-intensive process for many NLP tasks. Although there exist various methods to produce pseudo data labels, they are often task-specific and require a decent amount of labeled data to start with. Recently, the immense language model GPT-3 with 175 billion parameters has achieved tremendous improvement across many few-shot learning tasks. In this paper, we explore ways to leverage GPT-3 as a low-cost data labeler to train other models. We find that, to make the downstream model achieve the same performance on a variety of NLU and NLG tasks, it costs 5","authors_text":"Chenguang Zhu, Michael Zeng, Shuohang Wang, Yang Liu, Yichong Xu","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-08-30T19:18:24Z","title":"Want To Reduce Labeling Cost? GPT-3 Can Help"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2108.13487","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:dcba12e91fd585872917c90e31cb6e717d157e7a3df36ef93ed1fb1fa79c7831","target":"record","created_at":"2026-07-05T03:10:14Z","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":"473dbefe78a3359a326f11d846c322e81c701acd2a0e61cbe53fe03cd3e8c588","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-08-30T19:18:24Z","title_canon_sha256":"47cdeb28952efc8f094f3c301d6a2a877892d1b60c1e5fa0347e2a2966725b48"},"schema_version":"1.0","source":{"id":"2108.13487","kind":"arxiv","version":1}},"canonical_sha256":"e6fe284b5fa29e5c85fe12fa46f5a0bcd78d5f6c2daa5dbff00dca8af435b8d6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e6fe284b5fa29e5c85fe12fa46f5a0bcd78d5f6c2daa5dbff00dca8af435b8d6","first_computed_at":"2026-07-05T03:10:14.687070Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:10:14.687070Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Kak8+6vOG0fp9OJcaI1EUvX01DViqF1Ea/YLRFl6rhNsvNp6xHH1Gfe2XWJTMexPPlJ5bYToZootYM5+TsjEDg==","signature_status":"signed_v1","signed_at":"2026-07-05T03:10:14.687572Z","signed_message":"canonical_sha256_bytes"},"source_id":"2108.13487","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:dcba12e91fd585872917c90e31cb6e717d157e7a3df36ef93ed1fb1fa79c7831","sha256:ec94a272fc184d34c35e8123ba552837c2fe3f5425f1c83f8c6f1e5518baf916"],"state_sha256":"053d975c9edf6e7fbeafc21253998c10d0fe5fb5bf8d76284270a53f34b46f9e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TlofpSIWMyV8FWogI3rQJCB/1IZyZMzPv6+GEG2qu2mAsLOHzT+JvyzFLvT5BDutFH2irnGbJkN1SG9fpT3lCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T17:20:00.888729Z","bundle_sha256":"975da647dd3ae1dc21383a5cd495d1e0bf85076be577ca57f8b3aaa6b54c8674"}}