{"as_of":"2026-08-08T18:19:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e67bf8aeaa8defc063f4ae186e4aa2c654e5f3088d8a361bd60bb1cab29257df","coverage":[{"denominator":11,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":11,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T20:44:00.382918Z","state":"measured"},{"denominator":11,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":11,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2509.08381/citation-record","integrity":"/paper/2509.08381/integrity","json":"/paper/2509.08381/citation-record.json","paper":"/paper/2509.08381"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T20:43:59.601783Z","title":"Structured information extraction from scientific text with large language models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.08381","last_updated":"2025-09-10T08:19:07Z","snapshot_observed_at":"2026-08-04T20:43:52.461435Z","submitted_at":"2025-09-10T08:19:07Z","title":"Low-Resource Fine-Tuning for Multi-Task Structured Information Extraction with a Billion-Parameter Instruction-Tuned Model","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-04T20:43:59.601783Z"},"links":{"citing_paper":"/paper/2509.08381"},"observation_digest":"sha256:837a4de78e72defc44574090af04614bf4b06697d4e78aa1f1324be4db0aefc1","observation_id":"480b69a9-09e3-44c3-a206-97bc81f0557d","resolution":{"observed_at":"2026-08-04T20:43:59.601783Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.18653/v1/2024.bionlp-1.21","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Get the best out of 1B LLMs: Insights from information extraction on clinical documents,","venue":null,"work_id":"a1c58662-d546-4572-88f2-2220c39796a1","year":2024},"citing_paper":{"arxiv_id":"2509.08381","last_updated":"2025-09-10T08:19:07Z","snapshot_observed_at":"2026-08-04T20:43:52.461435Z","submitted_at":"2025-09-10T08:19:07Z","title":"Low-Resource Fine-Tuning for Multi-Task Structured Information Extraction with a Billion-Parameter Instruction-Tuned Model","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-04T20:43:59.670338Z"},"links":{"citing_paper":"/paper/2509.08381"},"observation_digest":"sha256:6f69cbbc5dee9acac40eadc524e4a73ff55cbae88e05a1912d3c6db2cacc6cab","observation_id":"38b700db-61a2-44b8-9f77-953009287af9","resolution":{"observed_at":"2026-08-04T20:44:00.585371Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.02953","last_updated":"2025-06-08T06:16:17Z","snapshot_observed_at":"2026-07-06T16:27:47.445841Z","submitted_at":"2023-10-04T16:44:23Z","title":"JsonTuning: Towards Generalizable, Robust, and Controllable Instruction Tuning","version":6},"cited_work":{"arxiv_id":"2310.02953","doi":null,"metadata_source":"pith","pith_arxiv_id":"2310.02953","snapshot_observed_at":"2026-08-04T20:44:00.750789Z","title":"JsonTuning: Towards Generalizable, Robust, and Controllable Instruction Tuning","venue":"cs.CL","work_id":"2e6b8121-43a2-4dd6-8d78-80b150d702b1","year":2023},"citing_paper":{"arxiv_id":"2509.08381","last_updated":"2025-09-10T08:19:07Z","snapshot_observed_at":"2026-08-04T20:43:52.461435Z","submitted_at":"2025-09-10T08:19:07Z","title":"Low-Resource Fine-Tuning for Multi-Task Structured Information Extraction with a Billion-Parameter Instruction-Tuned Model","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-04T20:43:59.763424Z"},"links":{"cited_paper":"/paper/2310.02953","citing_paper":"/paper/2509.08381"},"observation_digest":"sha256:f5f949a1ed6062d79de0727f7a660a00e40d8cb7cf2d2147c57a7fbebfb8d730","observation_id":"26b963d0-a186-443b-b28b-d305450ca9b3","resolution":{"observed_at":"2026-08-04T20:44:00.806998Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T20:44:01.471043Z","title":"Instruction tuning for on-demand information extraction,","venue":null,"work_id":"f680e781-6024-4c88-a3cf-bd493503efac","year":2023},"citing_paper":{"arxiv_id":"2509.08381","last_updated":"2025-09-10T08:19:07Z","snapshot_observed_at":"2026-08-04T20:43:52.461435Z","submitted_at":"2025-09-10T08:19:07Z","title":"Low-Resource Fine-Tuning for Multi-Task Structured Information Extraction with a Billion-Parameter Instruction-Tuned Model","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-04T20:43:59.844553Z"},"links":{"citing_paper":"/paper/2509.08381"},"observation_digest":"sha256:59d46f1d2391cd591a464bd639e085ae9e44e0be0e4daa431f9af2558a7eb54a","observation_id":"12a7fe92-9652-41f3-9cbf-3431718fc80d","resolution":{"observed_at":"2026-08-04T20:44:01.511056Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T20:44:01.301811Z","title":"Advancing entity recognition in biomedicine via instruction-based approaches,","venue":null,"work_id":"7ac52fad-e573-4d5e-9f61-9bde23347361","year":2024},"citing_paper":{"arxiv_id":"2509.08381","last_updated":"2025-09-10T08:19:07Z","snapshot_observed_at":"2026-08-04T20:43:52.461435Z","submitted_at":"2025-09-10T08:19:07Z","title":"Low-Resource Fine-Tuning for Multi-Task Structured Information Extraction with a Billion-Parameter Instruction-Tuned Model","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-04T20:43:59.940385Z"},"links":{"citing_paper":"/paper/2509.08381"},"observation_digest":"sha256:fd0ab62e59940ffd72aebb9cb59dc5da1232d876ea2da93a8640f8ebb9f143e0","observation_id":"5f99ac5d-b92f-40bf-b271-dcbaec69c4a3","resolution":{"observed_at":"2026-08-04T20:44:01.369949Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.02712","last_updated":"2024-04-03T14:29:41Z","snapshot_observed_at":"2026-07-06T17:39:41.498480Z","submitted_at":"2024-03-05T07:08:06Z","title":"Breeze-7B Technical Report","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.02712","snapshot_observed_at":"2026-08-04T20:44:00.002645Z","title":"Breeze-7B technical report,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.08381","last_updated":"2025-09-10T08:19:07Z","snapshot_observed_at":"2026-08-04T20:43:52.461435Z","submitted_at":"2025-09-10T08:19:07Z","title":"Low-Resource Fine-Tuning for Multi-Task Structured Information Extraction with a Billion-Parameter Instruction-Tuned Model","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-04T20:44:00.002645Z"},"links":{"cited_paper":"/paper/2403.02712","citing_paper":"/paper/2509.08381"},"observation_digest":"sha256:a96d0a7f73f56e0890f283150ac243ea207511fe6c5e78ad843bce456dc1bb9d","observation_id":"787fbc5e-9f43-41f5-8e3e-4a99d6dbf9b1","resolution":{"observed_at":"2026-08-04T20:44:00.002645Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.08085","last_updated":"2023-04-17T09:00:50Z","snapshot_observed_at":"2026-08-07T15:25:41.220313Z","submitted_at":"2023-04-17T09:00:50Z","title":"InstructUIE: Multi-task Instruction Tuning for Unified Information Extraction","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.08085","snapshot_observed_at":"2026-08-04T20:44:00.087316Z","title":"InstructUIE: Multi-task instruction tuning for unified information extraction,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.08381","last_updated":"2025-09-10T08:19:07Z","snapshot_observed_at":"2026-08-04T20:43:52.461435Z","submitted_at":"2025-09-10T08:19:07Z","title":"Low-Resource Fine-Tuning for Multi-Task Structured Information Extraction with a Billion-Parameter Instruction-Tuned Model","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-04T20:44:00.087316Z"},"links":{"cited_paper":"/paper/2304.08085","citing_paper":"/paper/2509.08381"},"observation_digest":"sha256:6729da5aa3c9bc1e0c684707069242c16883eab234a7311b05b5e76ad30627d9","observation_id":"966f9ff6-a1bb-4b6d-9d8b-4dc2fe259db0","resolution":{"observed_at":"2026-08-04T20:44:00.087316Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T20:44:01.133556Z","title":"LlamaFactory: Unified efficient fine-tuning of 100+ language models,","venue":null,"work_id":"355711b3-dae9-4fb7-9f20-1023d0d3b09a","year":null},"citing_paper":{"arxiv_id":"2509.08381","last_updated":"2025-09-10T08:19:07Z","snapshot_observed_at":"2026-08-04T20:43:52.461435Z","submitted_at":"2025-09-10T08:19:07Z","title":"Low-Resource Fine-Tuning for Multi-Task Structured Information Extraction with a Billion-Parameter Instruction-Tuned Model","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-04T20:44:00.181128Z"},"links":{"citing_paper":"/paper/2509.08381"},"observation_digest":"sha256:ebf8dd9913b758b0cca66ca39cbcf3338c41d66937858e237f562e3f5fe00d84","observation_id":"af4af074-7110-4728-8bf4-65ad41a14062","resolution":{"observed_at":"2026-08-04T20:44:01.222988Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T20:44:00.906350Z","title":"Learning to extract structured entities using language models,","venue":null,"work_id":"f0455392-c961-4d27-aeaa-0595de4547d5","year":2024},"citing_paper":{"arxiv_id":"2509.08381","last_updated":"2025-09-10T08:19:07Z","snapshot_observed_at":"2026-08-04T20:43:52.461435Z","submitted_at":"2025-09-10T08:19:07Z","title":"Low-Resource Fine-Tuning for Multi-Task Structured Information Extraction with a Billion-Parameter Instruction-Tuned Model","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-04T20:44:00.291684Z"},"links":{"citing_paper":"/paper/2509.08381"},"observation_digest":"sha256:717b419fd764218a7e9ebb7012efbc93cb08e82305b4d7ba8edbd38e14c574a6","observation_id":"a55c07cd-fdc5-45f6-86a5-c31a362a97e3","resolution":{"observed_at":"2026-08-04T20:44:00.964171Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.15115","last_updated":"2025-01-03T02:18:21Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-12-19T17:56:09Z","title":"Qwen2.5 Technical Report","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.15115","snapshot_observed_at":"2026-08-04T20:44:00.382918Z","title":"Qwen2.5 technical report,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.08381","last_updated":"2025-09-10T08:19:07Z","snapshot_observed_at":"2026-08-04T20:43:52.461435Z","submitted_at":"2025-09-10T08:19:07Z","title":"Low-Resource Fine-Tuning for Multi-Task Structured Information Extraction with a Billion-Parameter Instruction-Tuned Model","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-04T20:44:00.382918Z"},"links":{"cited_paper":"/paper/2412.15115","citing_paper":"/paper/2509.08381"},"observation_digest":"sha256:1c22ee4310f13a0870a5a5d1e5e8f6175da10c97b1c29cd6dbdebc2c15560c43","observation_id":"f7acb8b1-17cd-4e8c-a1d4-2cc602b2ea30","resolution":{"observed_at":"2026-08-04T20:44:00.382918Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.13372","last_updated":"2024-06-27T22:44:48Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-03-20T08:08:54Z","title":"LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.13372","snapshot_observed_at":"2026-08-04T20:44:00.224276Z","title":"Available: http://arxiv.org/abs/2403.13372","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.08381","last_updated":"2025-09-10T08:19:07Z","snapshot_observed_at":"2026-08-04T20:43:52.461435Z","submitted_at":"2025-09-10T08:19:07Z","title":"Low-Resource Fine-Tuning for Multi-Task Structured Information Extraction with a Billion-Parameter Instruction-Tuned Model","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-04T20:44:00.224276Z"},"links":{"cited_paper":"/paper/2403.13372","citing_paper":"/paper/2509.08381"},"observation_digest":"sha256:ecb6bc1a388d65dd4fe778b94c310a9713e87026f3607bd14f645547032f819b","observation_id":"e31d9d94-7aa2-4803-a8f1-da450b4fe713","resolution":{"observed_at":"2026-08-04T20:44:00.224276Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2509.08381","last_updated":"2025-09-10T08:19:07Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-04T20:43:52.461435Z","submitted_at":"2025-09-10T08:19:07Z","title":"Low-Resource Fine-Tuning for Multi-Task Structured Information Extraction with a Billion-Parameter Instruction-Tuned Model"},"reference_resolution":{"displayed":11,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":4,"verified_exact":2,"verified_fuzzy":4},"total_outbound_references":11},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 11 of 11 outbound references and 0 inbound Pith citation observations for arXiv:2509.08381."}