{"as_of":"2026-08-18T16:10:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e29b0441878c14f9f5badfeab5b7f6208da77295242af40588163020c3c88505","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":7,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":7,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+00:00","state":"measured"},{"denominator":7,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":7,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T11:24:12.479838Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-07T14:35:03.953265Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2311.00287","last_updated":"2025-01-24T23:46:07Z","snapshot_observed_at":"2026-08-16T14:46:59.036907Z","submitted_at":"2023-11-01T04:37:28Z","title":"Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.00287","snapshot_observed_at":"2026-08-10T18:43:53.727559Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.11067","last_updated":"2025-01-19T14:58:35Z","snapshot_observed_at":"2026-08-16T16:31:48.834734Z","submitted_at":"2025-01-19T14:58:35Z","title":"IntellAgent: A Multi-Agent Framework for Evaluating Conversational AI Systems","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-10T18:43:53.727559Z"},"links":{"cited_paper":"/paper/2311.00287","citing_paper":"/paper/2501.11067"},"observation_digest":"sha256:eab781b73746eb2a35af07f674cda579d2646db2c99c47b575323166649ba746","observation_id":"40742cb0-949e-4874-af72-80a351e560f8","resolution":{"observed_at":"2026-08-10T18:43:53.727559Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.00287","last_updated":"2025-01-24T23:46:07Z","snapshot_observed_at":"2026-08-16T14:46:59.036907Z","submitted_at":"2023-11-01T04:37:28Z","title":"Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.00287","snapshot_observed_at":"2026-08-16T11:24:12.479838Z","title":"Knowledge-infused prompt- ing: Assessing and advancing clinical text data gen- eration with large language models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2504.15585","last_updated":"2025-06-09T02:36:20Z","snapshot_observed_at":"2026-08-17T21:55:02.668814Z","submitted_at":"2025-04-22T05:02:49Z","title":"A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment","version":4},"reference_index":167,"source":"pdf_text","source_observed_at":"2026-08-16T11:24:12.479838Z"},"links":{"cited_paper":"/paper/2311.00287","citing_paper":"/paper/2504.15585"},"observation_digest":"sha256:c8748173d7cca06784eecbe0be5e1d943f0b91274f1d907b85e0fb1bdff74bb2","observation_id":"35be5b22-7993-428d-be3c-708b04bda951","resolution":{"observed_at":"2026-08-16T11:24:12.479838Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.00287","last_updated":"2025-01-24T23:46:07Z","snapshot_observed_at":"2026-08-16T14:46:59.036907Z","submitted_at":"2023-11-01T04:37:28Z","title":"Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.00287","snapshot_observed_at":"2026-08-16T11:15:56.256134Z","title":"more difficult","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2504.16188","last_updated":"2025-04-22T18:25:17Z","snapshot_observed_at":"2026-08-16T11:07:56.742026Z","submitted_at":"2025-04-22T18:25:17Z","title":"FinNLI: Novel Dataset for Multi-Genre Financial Natural Language Inference Benchmarking","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-16T11:15:56.256134Z"},"links":{"cited_paper":"/paper/2311.00287","citing_paper":"/paper/2504.16188"},"observation_digest":"sha256:3709a7179c550e377e6042bfb8d895b40a39b3a54378bba05b8a8ab255fc74c0","observation_id":"2495b12b-34bf-48b3-a9c8-f67075b5a38c","resolution":{"observed_at":"2026-08-16T11:15:56.256134Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.00287","last_updated":"2025-01-24T23:46:07Z","snapshot_observed_at":"2026-08-16T14:46:59.036907Z","submitted_at":"2023-11-01T04:37:28Z","title":"Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.00287","snapshot_observed_at":"2026-08-15T23:25:52.613724Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.04732","last_updated":"2025-05-07T18:43:57Z","snapshot_observed_at":"2026-08-16T06:45:15.844745Z","submitted_at":"2025-05-07T18:43:57Z","title":"QBD-RankedDataGen: Generating Custom Ranked Datasets for Improving Query-By-Document Search Using LLM-Reranking with Reduced Human Effort","version":1},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-15T23:25:52.613724Z"},"links":{"cited_paper":"/paper/2311.00287","citing_paper":"/paper/2505.04732"},"observation_digest":"sha256:78c1bf1f5a9cdb22e95e8bf61850bd3b71cf6e3b913fc765e91bab88fd719307","observation_id":"77d9702b-0c81-4732-a669-b834022c52f7","resolution":{"observed_at":"2026-08-15T23:25:52.613724Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.00287","last_updated":"2025-01-24T23:46:07Z","snapshot_observed_at":"2026-08-16T14:46:59.036907Z","submitted_at":"2023-11-01T04:37:28Z","title":"Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.00287","snapshot_observed_at":"2026-08-15T21:12:57.914308Z","title":", Cui, H","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.10472","last_updated":"2025-05-15T16:23:21Z","snapshot_observed_at":"2026-08-18T08:21:27.201426Z","submitted_at":"2025-05-15T16:23:21Z","title":"Large Language Models for Cancer Communication: Evaluating Linguistic Quality, Safety, and Accessibility in Generative AI","version":1},"reference_index":81,"source":"arxiv_source","source_observed_at":"2026-08-15T21:12:57.914308Z"},"links":{"cited_paper":"/paper/2311.00287","citing_paper":"/paper/2505.10472"},"observation_digest":"sha256:a9baa002acea71127fd82a2c4664a7225d00623fa3993459723c299ec1a4efe1","observation_id":"323b7f8e-4442-475e-b126-570aaedae265","resolution":{"observed_at":"2026-08-15T21:12:57.914308Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.00287","last_updated":"2025-01-24T23:46:07Z","snapshot_observed_at":"2026-08-16T14:46:59.036907Z","submitted_at":"2023-11-01T04:37:28Z","title":"Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models","version":2},"cited_work":{"arxiv_id":"2311.00287","doi":null,"metadata_source":"pith","pith_arxiv_id":"2311.00287","snapshot_observed_at":"2026-08-07T14:35:03.953265Z","title":"Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models","venue":"cs.CL","work_id":"5941bcfb-e55f-4082-9b11-65c5d632fdae","year":2023},"citing_paper":{"arxiv_id":"2505.18464","last_updated":"2025-05-24T02:07:32Z","snapshot_observed_at":"2026-08-18T08:22:00.171287Z","submitted_at":"2025-05-24T02:07:32Z","title":"From Reddit to Generative AI: Evaluating Large Language Models for Anxiety Support Fine-tuned on Social Media Data","version":1},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-07T14:35:03.869792Z"},"links":{"cited_paper":"/paper/2311.00287","citing_paper":"/paper/2505.18464"},"observation_digest":"sha256:f880aebdfc172a421bc852d84747fc721bbd64fbefd056fa6050181ae80533be","observation_id":"83502ff5-3130-423b-84b9-dda1383c9717","resolution":{"observed_at":"2026-08-07T14:35:03.959101Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.00287","last_updated":"2025-01-24T23:46:07Z","snapshot_observed_at":"2026-08-16T14:46:59.036907Z","submitted_at":"2023-11-01T04:37:28Z","title":"Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.00287","snapshot_observed_at":"2026-08-07T19:41:10.891706Z","title":"Xu et al","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.05993","last_updated":"2026-08-06T13:04:42Z","snapshot_observed_at":"2026-08-17T18:48:23.526219Z","submitted_at":"2026-08-06T13:04:42Z","title":"Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T19:41:10.891706Z"},"links":{"cited_paper":"/paper/2311.00287","citing_paper":"/paper/2608.05993"},"observation_digest":"sha256:dbc8ca645f5df8f577cc1d63cfe96f09ebce00c50131af440597fdd0713e409a","observation_id":"81695e5a-d2e4-4b49-9e68-34a3302a49f6","resolution":{"observed_at":"2026-08-07T19:41:10.891706Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2311.00287/citation-record","integrity":"/paper/2311.00287/integrity","json":"/paper/2311.00287/citation-record.json","paper":"/paper/2311.00287"},"outbound":[],"paper":{"arxiv_id":"2311.00287","last_updated":"2025-01-24T23:46:07Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-16T14:46:59.036907Z","submitted_at":"2023-11-01T04:37:28Z","title":"Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2311.00287."}