{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:KUFXSU7QBHBLECDO4RPQCLYWGR","short_pith_number":"pith:KUFXSU7Q","canonical_record":{"source":{"id":"2311.00287","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-11-01T04:37:28Z","cross_cats_sorted":["cs.AI","cs.LG","q-bio.QM"],"title_canon_sha256":"d17842cf86f19f970f7632fc809ed5d821193485a284d4088430a06bf0ad3ed2","abstract_canon_sha256":"05abc73b7a38b8a38f53fa032af7009ba745c6aaa3c08f097646658ccf0b1578"},"schema_version":"1.0"},"canonical_sha256":"550b7953f009c2b2086ee45f012f163445accb93d2b4e49cb4b2b60b7f8e3987","source":{"kind":"arxiv","id":"2311.00287","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.00287","created_at":"2026-07-05T10:05:07Z"},{"alias_kind":"arxiv_version","alias_value":"2311.00287v2","created_at":"2026-07-05T10:05:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.00287","created_at":"2026-07-05T10:05:07Z"},{"alias_kind":"pith_short_12","alias_value":"KUFXSU7QBHBL","created_at":"2026-07-05T10:05:07Z"},{"alias_kind":"pith_short_16","alias_value":"KUFXSU7QBHBLECDO","created_at":"2026-07-05T10:05:07Z"},{"alias_kind":"pith_short_8","alias_value":"KUFXSU7Q","created_at":"2026-07-05T10:05:07Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:KUFXSU7QBHBLECDO4RPQCLYWGR","target":"record","payload":{"canonical_record":{"source":{"id":"2311.00287","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-11-01T04:37:28Z","cross_cats_sorted":["cs.AI","cs.LG","q-bio.QM"],"title_canon_sha256":"d17842cf86f19f970f7632fc809ed5d821193485a284d4088430a06bf0ad3ed2","abstract_canon_sha256":"05abc73b7a38b8a38f53fa032af7009ba745c6aaa3c08f097646658ccf0b1578"},"schema_version":"1.0"},"canonical_sha256":"550b7953f009c2b2086ee45f012f163445accb93d2b4e49cb4b2b60b7f8e3987","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:05:07.905050Z","signature_b64":"J4HgLOCwxnkeK7IcekZ8t65gKHr4GY8yoX2OxqrfyAIVmqacEyPnduw5cWFJTtbkuGsAyuSsTR8XxRhpgWNHCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"550b7953f009c2b2086ee45f012f163445accb93d2b4e49cb4b2b60b7f8e3987","last_reissued_at":"2026-07-05T10:05:07.904642Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:05:07.904642Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2311.00287","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:05:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Q4jskPK72F9ygt7iWZ6WWVW/5qaZAw5AJOFT/s2SqE9RCQtDVUdvkkSHvHp5OtMjyiB+bt5IQ0lmk4akCWxjBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T20:40:37.008001Z"},"content_sha256":"358d84c82f35358d2384e4b391814388c8108ac9eb7e81b4fec3ab99dac43a01","schema_version":"1.0","event_id":"sha256:358d84c82f35358d2384e4b391814388c8108ac9eb7e81b4fec3ab99dac43a01"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:KUFXSU7QBHBLECDO4RPQCLYWGR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG","q-bio.QM"],"primary_cat":"cs.CL","authors_text":"Carl Yang, Hejie Cui, Joyce Ho, Ran Xu, Wei Jin, Wenqi Shi, Xuan Kan, Yuchen Zhuang, Yue Yu","submitted_at":"2023-11-01T04:37:28Z","abstract_excerpt":"Clinical natural language processing requires methods that can address domain-specific challenges, such as complex medical terminology and clinical contexts. Recently, large language models (LLMs) have shown promise in this domain. Yet, their direct deployment can lead to privacy issues and are constrained by resources. To address this challenge, we delve into synthetic clinical text generation using LLMs for clinical NLP tasks. We propose an innovative, resource-efficient approach, ClinGen, which infuses knowledge into the process. Our model involves clinical knowledge extraction and context-"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.00287","kind":"arxiv","version":2},"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/2311.00287/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":"2026-07-05T10:05:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JG+3iPlvudUmosyYh7d8GJmmuDVjD7jhSngQDZRqWROMTr3qTiVna7fvWi9y4FPsyCqi0YGKAiWnS4yfYt61BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T20:40:37.008741Z"},"content_sha256":"0e966ecb389e1af31e1c6e0d7b0d792af1b3a00792dd329576509715e9c97606","schema_version":"1.0","event_id":"sha256:0e966ecb389e1af31e1c6e0d7b0d792af1b3a00792dd329576509715e9c97606"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KUFXSU7QBHBLECDO4RPQCLYWGR/bundle.json","state_url":"https://pith.science/pith/KUFXSU7QBHBLECDO4RPQCLYWGR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KUFXSU7QBHBLECDO4RPQCLYWGR/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":"2026-08-08T20:40:37Z","links":{"resolver":"https://pith.science/pith/KUFXSU7QBHBLECDO4RPQCLYWGR","bundle":"https://pith.science/pith/KUFXSU7QBHBLECDO4RPQCLYWGR/bundle.json","state":"https://pith.science/pith/KUFXSU7QBHBLECDO4RPQCLYWGR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KUFXSU7QBHBLECDO4RPQCLYWGR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:KUFXSU7QBHBLECDO4RPQCLYWGR","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":"05abc73b7a38b8a38f53fa032af7009ba745c6aaa3c08f097646658ccf0b1578","cross_cats_sorted":["cs.AI","cs.LG","q-bio.QM"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-11-01T04:37:28Z","title_canon_sha256":"d17842cf86f19f970f7632fc809ed5d821193485a284d4088430a06bf0ad3ed2"},"schema_version":"1.0","source":{"id":"2311.00287","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.00287","created_at":"2026-07-05T10:05:07Z"},{"alias_kind":"arxiv_version","alias_value":"2311.00287v2","created_at":"2026-07-05T10:05:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.00287","created_at":"2026-07-05T10:05:07Z"},{"alias_kind":"pith_short_12","alias_value":"KUFXSU7QBHBL","created_at":"2026-07-05T10:05:07Z"},{"alias_kind":"pith_short_16","alias_value":"KUFXSU7QBHBLECDO","created_at":"2026-07-05T10:05:07Z"},{"alias_kind":"pith_short_8","alias_value":"KUFXSU7Q","created_at":"2026-07-05T10:05:07Z"}],"graph_snapshots":[{"event_id":"sha256:0e966ecb389e1af31e1c6e0d7b0d792af1b3a00792dd329576509715e9c97606","target":"graph","created_at":"2026-07-05T10:05:07Z","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/2311.00287/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Clinical natural language processing requires methods that can address domain-specific challenges, such as complex medical terminology and clinical contexts. Recently, large language models (LLMs) have shown promise in this domain. Yet, their direct deployment can lead to privacy issues and are constrained by resources. To address this challenge, we delve into synthetic clinical text generation using LLMs for clinical NLP tasks. We propose an innovative, resource-efficient approach, ClinGen, which infuses knowledge into the process. Our model involves clinical knowledge extraction and context-","authors_text":"Carl Yang, Hejie Cui, Joyce Ho, Ran Xu, Wei Jin, Wenqi Shi, Xuan Kan, Yuchen Zhuang, Yue Yu","cross_cats":["cs.AI","cs.LG","q-bio.QM"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-11-01T04:37:28Z","title":"Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.00287","kind":"arxiv","version":2},"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":"sha256:358d84c82f35358d2384e4b391814388c8108ac9eb7e81b4fec3ab99dac43a01","target":"record","created_at":"2026-07-05T10:05:07Z","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":"05abc73b7a38b8a38f53fa032af7009ba745c6aaa3c08f097646658ccf0b1578","cross_cats_sorted":["cs.AI","cs.LG","q-bio.QM"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-11-01T04:37:28Z","title_canon_sha256":"d17842cf86f19f970f7632fc809ed5d821193485a284d4088430a06bf0ad3ed2"},"schema_version":"1.0","source":{"id":"2311.00287","kind":"arxiv","version":2}},"canonical_sha256":"550b7953f009c2b2086ee45f012f163445accb93d2b4e49cb4b2b60b7f8e3987","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"550b7953f009c2b2086ee45f012f163445accb93d2b4e49cb4b2b60b7f8e3987","first_computed_at":"2026-07-05T10:05:07.904642Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:05:07.904642Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"J4HgLOCwxnkeK7IcekZ8t65gKHr4GY8yoX2OxqrfyAIVmqacEyPnduw5cWFJTtbkuGsAyuSsTR8XxRhpgWNHCA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:05:07.905050Z","signed_message":"canonical_sha256_bytes"},"source_id":"2311.00287","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:358d84c82f35358d2384e4b391814388c8108ac9eb7e81b4fec3ab99dac43a01","sha256:0e966ecb389e1af31e1c6e0d7b0d792af1b3a00792dd329576509715e9c97606"],"state_sha256":"0d96ae4f0c7bb579539144a2d9d9a84b0dddf0d47e215cff7440e45b0df0fc5f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qGbhk8AwDna/+kEZSsa3UCYRsBi/HZjDa5ktXSBUUEhB0wMqH0IzRaFfwXJGE9IQanirSPTeCj/Jsclq/vL7CQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T20:40:37.013656Z","bundle_sha256":"b29580011f7eda6f8811dc4a4e414ce2f7b531084a4b8570f7a265eda8ed07e3"}}