{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:WRQFCMCRT5UV34EB7LQRQA65LE","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":"ed11857b304e90e52a6454ff2b5b6c2b78196cffe646dcd5b8a272ae3a941d6e","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-02-02T00:48:26Z","title_canon_sha256":"9793896cf4db251885a64ad8ed2cd1bbdc48ebc5d390abeddde71265689c3302"},"schema_version":"1.0","source":{"id":"2202.00828","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2202.00828","created_at":"2026-07-05T03:53:28Z"},{"alias_kind":"arxiv_version","alias_value":"2202.00828v1","created_at":"2026-07-05T03:53:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.00828","created_at":"2026-07-05T03:53:28Z"},{"alias_kind":"pith_short_12","alias_value":"WRQFCMCRT5UV","created_at":"2026-07-05T03:53:28Z"},{"alias_kind":"pith_short_16","alias_value":"WRQFCMCRT5UV34EB","created_at":"2026-07-05T03:53:28Z"},{"alias_kind":"pith_short_8","alias_value":"WRQFCMCR","created_at":"2026-07-05T03:53:28Z"}],"graph_snapshots":[{"event_id":"sha256:2f7e10d5943d710e4c43e7d245ea0c60fac26f379882f7515f0f90a3cab5ddb0","target":"graph","created_at":"2026-07-05T03:53:28Z","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/2202.00828/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We demonstrate that co-training (Blum & Mitchell, 1998) can improve the performance of prompt-based learning by using unlabeled data. While prompting has emerged as a promising paradigm for few-shot and zero-shot learning, it is often brittle and requires much larger models compared to the standard supervised setup. We find that co-training makes it possible to improve the original prompt model and at the same time learn a smaller, downstream task-specific model. In the case where we only have partial access to a prompt model (e.g., output probabilities from GPT-3 (Brown et al., 2020)) we lear","authors_text":"David Sontag, Hunter Lang, Monica Agrawal, Yoon Kim","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-02-02T00:48:26Z","title":"Co-training Improves Prompt-based Learning for Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.00828","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":"sha256:102609410419917fac1d9f05971da6bd3eef9e93675a54957093fdfe73425fc9","target":"record","created_at":"2026-07-05T03:53:28Z","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":"ed11857b304e90e52a6454ff2b5b6c2b78196cffe646dcd5b8a272ae3a941d6e","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-02-02T00:48:26Z","title_canon_sha256":"9793896cf4db251885a64ad8ed2cd1bbdc48ebc5d390abeddde71265689c3302"},"schema_version":"1.0","source":{"id":"2202.00828","kind":"arxiv","version":1}},"canonical_sha256":"b4605130519f695df081fae11803dd593751aa8219eb936e2f48b2c0ef9944c6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b4605130519f695df081fae11803dd593751aa8219eb936e2f48b2c0ef9944c6","first_computed_at":"2026-07-05T03:53:28.046779Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:53:28.046779Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"7evRr8Eodkos2K1fu6xgr5FeciK1Arpf0beohmJHuuv1uEpTgL7uEUYNDGBSGYEdShAkOvj1h4Sp2QWtyEKQAg==","signature_status":"signed_v1","signed_at":"2026-07-05T03:53:28.047236Z","signed_message":"canonical_sha256_bytes"},"source_id":"2202.00828","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:102609410419917fac1d9f05971da6bd3eef9e93675a54957093fdfe73425fc9","sha256:2f7e10d5943d710e4c43e7d245ea0c60fac26f379882f7515f0f90a3cab5ddb0"],"state_sha256":"2ecc3718e05e3f68b240ae797e38ea3b400504bfb21db85efc9af5b785787673"}