{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:2ERTLTY5PN6LTVVYFVKOR332DK","short_pith_number":"pith:2ERTLTY5","canonical_record":{"source":{"id":"2203.08410","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-03-16T05:56:08Z","cross_cats_sorted":["cs.IR"],"title_canon_sha256":"30a3d48dfa3cc5852e5a31c82a23ada9e20e5f64f90c0aed806db3be34801873","abstract_canon_sha256":"799daf9ba66d6c1cac8d8325331591a26f7627aa5504140f3d80ce18c13b9c93"},"schema_version":"1.0"},"canonical_sha256":"d12335cf1d7b7cb9d6b82d54e8ef7a1abdffdc875a1b87272a12b11c532e960d","source":{"kind":"arxiv","id":"2203.08410","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2203.08410","created_at":"2026-07-05T05:13:32Z"},{"alias_kind":"arxiv_version","alias_value":"2203.08410v3","created_at":"2026-07-05T05:13:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.08410","created_at":"2026-07-05T05:13:32Z"},{"alias_kind":"pith_short_12","alias_value":"2ERTLTY5PN6L","created_at":"2026-07-05T05:13:32Z"},{"alias_kind":"pith_short_16","alias_value":"2ERTLTY5PN6LTVVY","created_at":"2026-07-05T05:13:32Z"},{"alias_kind":"pith_short_8","alias_value":"2ERTLTY5","created_at":"2026-07-05T05:13:32Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:2ERTLTY5PN6LTVVYFVKOR332DK","target":"record","payload":{"canonical_record":{"source":{"id":"2203.08410","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-03-16T05:56:08Z","cross_cats_sorted":["cs.IR"],"title_canon_sha256":"30a3d48dfa3cc5852e5a31c82a23ada9e20e5f64f90c0aed806db3be34801873","abstract_canon_sha256":"799daf9ba66d6c1cac8d8325331591a26f7627aa5504140f3d80ce18c13b9c93"},"schema_version":"1.0"},"canonical_sha256":"d12335cf1d7b7cb9d6b82d54e8ef7a1abdffdc875a1b87272a12b11c532e960d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:13:32.777930Z","signature_b64":"iSL8+dz4yfwekJ+bmudcqzZxWKVtAlpXK+pZ//5u8g6U0aoohtG09i/fSmt1WLNilZkyNQpN8/Is5PBXoya3AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d12335cf1d7b7cb9d6b82d54e8ef7a1abdffdc875a1b87272a12b11c532e960d","last_reissued_at":"2026-07-05T05:13:32.777439Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:13:32.777439Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2203.08410","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:13:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"M3WcSxCApfpBcTSYUEV+7z4aGA2MP0RghcL9knwBHuiBIJ5L//7QpQ7bLURQrcxE2FpmzMgUnsi7qH+QyIwcBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T21:03:59.981883Z"},"content_sha256":"aafe4299365554645eb4b204499baca0e42563a4900628c16105071b07dea4ab","schema_version":"1.0","event_id":"sha256:aafe4299365554645eb4b204499baca0e42563a4900628c16105071b07dea4ab"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:2ERTLTY5PN6LTVVYFVKOR332DK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Thinking about GPT-3 In-Context Learning for Biomedical IE? Think Again","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.IR"],"primary_cat":"cs.CL","authors_text":"Bernal Jim\\'enez Guti\\'errez, Clay Washington, Huan Sun, Lang Li, Nikolas McNeal, You Chen, Yu Su","submitted_at":"2022-03-16T05:56:08Z","abstract_excerpt":"The strong few-shot in-context learning capability of large pre-trained language models (PLMs) such as GPT-3 is highly appealing for application domains such as biomedicine, which feature high and diverse demands of language technologies but also high data annotation costs. In this paper, we present the first systematic and comprehensive study to compare the few-shot performance of GPT-3 in-context learning with fine-tuning smaller (i.e., BERT-sized) PLMs on two highly representative biomedical information extraction tasks, named entity recognition and relation extraction. We follow the true f"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.08410","kind":"arxiv","version":3},"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/2203.08410/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":"2026-07-05T05:13:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HAoO+EOslFiw0SX6udIcaJaUu959TNTvtXBh7d3PvOSdTuaqKD4i5ZMZnnMPHPFgX9jaOdLISHpfFDkfYNRYBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T21:03:59.982700Z"},"content_sha256":"69ae2bb7f0997f4c541d83a51a3143a4261fe28f80e354712fdbb5ff2829a72f","schema_version":"1.0","event_id":"sha256:69ae2bb7f0997f4c541d83a51a3143a4261fe28f80e354712fdbb5ff2829a72f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2ERTLTY5PN6LTVVYFVKOR332DK/bundle.json","state_url":"https://pith.science/pith/2ERTLTY5PN6LTVVYFVKOR332DK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2ERTLTY5PN6LTVVYFVKOR332DK/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":"2026-08-12T21:03:59Z","links":{"resolver":"https://pith.science/pith/2ERTLTY5PN6LTVVYFVKOR332DK","bundle":"https://pith.science/pith/2ERTLTY5PN6LTVVYFVKOR332DK/bundle.json","state":"https://pith.science/pith/2ERTLTY5PN6LTVVYFVKOR332DK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2ERTLTY5PN6LTVVYFVKOR332DK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:2ERTLTY5PN6LTVVYFVKOR332DK","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":"799daf9ba66d6c1cac8d8325331591a26f7627aa5504140f3d80ce18c13b9c93","cross_cats_sorted":["cs.IR"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-03-16T05:56:08Z","title_canon_sha256":"30a3d48dfa3cc5852e5a31c82a23ada9e20e5f64f90c0aed806db3be34801873"},"schema_version":"1.0","source":{"id":"2203.08410","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2203.08410","created_at":"2026-07-05T05:13:32Z"},{"alias_kind":"arxiv_version","alias_value":"2203.08410v3","created_at":"2026-07-05T05:13:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.08410","created_at":"2026-07-05T05:13:32Z"},{"alias_kind":"pith_short_12","alias_value":"2ERTLTY5PN6L","created_at":"2026-07-05T05:13:32Z"},{"alias_kind":"pith_short_16","alias_value":"2ERTLTY5PN6LTVVY","created_at":"2026-07-05T05:13:32Z"},{"alias_kind":"pith_short_8","alias_value":"2ERTLTY5","created_at":"2026-07-05T05:13:32Z"}],"graph_snapshots":[{"event_id":"sha256:69ae2bb7f0997f4c541d83a51a3143a4261fe28f80e354712fdbb5ff2829a72f","target":"graph","created_at":"2026-07-05T05:13:32Z","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/2203.08410/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The strong few-shot in-context learning capability of large pre-trained language models (PLMs) such as GPT-3 is highly appealing for application domains such as biomedicine, which feature high and diverse demands of language technologies but also high data annotation costs. In this paper, we present the first systematic and comprehensive study to compare the few-shot performance of GPT-3 in-context learning with fine-tuning smaller (i.e., BERT-sized) PLMs on two highly representative biomedical information extraction tasks, named entity recognition and relation extraction. We follow the true f","authors_text":"Bernal Jim\\'enez Guti\\'errez, Clay Washington, Huan Sun, Lang Li, Nikolas McNeal, You Chen, Yu Su","cross_cats":["cs.IR"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-03-16T05:56:08Z","title":"Thinking about GPT-3 In-Context Learning for Biomedical IE? Think Again"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.08410","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:aafe4299365554645eb4b204499baca0e42563a4900628c16105071b07dea4ab","target":"record","created_at":"2026-07-05T05:13:32Z","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":"799daf9ba66d6c1cac8d8325331591a26f7627aa5504140f3d80ce18c13b9c93","cross_cats_sorted":["cs.IR"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-03-16T05:56:08Z","title_canon_sha256":"30a3d48dfa3cc5852e5a31c82a23ada9e20e5f64f90c0aed806db3be34801873"},"schema_version":"1.0","source":{"id":"2203.08410","kind":"arxiv","version":3}},"canonical_sha256":"d12335cf1d7b7cb9d6b82d54e8ef7a1abdffdc875a1b87272a12b11c532e960d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d12335cf1d7b7cb9d6b82d54e8ef7a1abdffdc875a1b87272a12b11c532e960d","first_computed_at":"2026-07-05T05:13:32.777439Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:13:32.777439Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"iSL8+dz4yfwekJ+bmudcqzZxWKVtAlpXK+pZ//5u8g6U0aoohtG09i/fSmt1WLNilZkyNQpN8/Is5PBXoya3AA==","signature_status":"signed_v1","signed_at":"2026-07-05T05:13:32.777930Z","signed_message":"canonical_sha256_bytes"},"source_id":"2203.08410","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:aafe4299365554645eb4b204499baca0e42563a4900628c16105071b07dea4ab","sha256:69ae2bb7f0997f4c541d83a51a3143a4261fe28f80e354712fdbb5ff2829a72f"],"state_sha256":"e123c737db3435ce21e24f6e6b4dbca171113ff47ca7674b54287cb6b38e8bbe"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+hua79FAeE/3a+lR8tx+EU4w+O9VH4983Mib3Imw1BhiS6ug6JylZTSQZQeoBj54HBtcEFbm1O3B/p7j6oqYAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-12T21:03:59.987781Z","bundle_sha256":"22974408602f79e4d8b7d4b53c851636dac108992d2646d66dea91b1da617b2f"}}