{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:GPCCWICNZ6MSRQQIEII2BDP7MC","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":"bafc910d6e25c8e8b46de54f72ccf01cbe0eae0f922d57813b0c8ed0f4d9ab07","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-05-26T17:16:11Z","title_canon_sha256":"d4b1d434633fc4efbdc39f716cda0f5f6f715377156c9c7aed2637ab355cbb23"},"schema_version":"1.0","source":{"id":"2305.17104","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.17104","created_at":"2026-07-05T06:14:21Z"},{"alias_kind":"arxiv_version","alias_value":"2305.17104v1","created_at":"2026-07-05T06:14:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.17104","created_at":"2026-07-05T06:14:21Z"},{"alias_kind":"pith_short_12","alias_value":"GPCCWICNZ6MS","created_at":"2026-07-05T06:14:21Z"},{"alias_kind":"pith_short_16","alias_value":"GPCCWICNZ6MSRQQI","created_at":"2026-07-05T06:14:21Z"},{"alias_kind":"pith_short_8","alias_value":"GPCCWICN","created_at":"2026-07-05T06:14:21Z"}],"graph_snapshots":[{"event_id":"sha256:5638d938fcff3943a69b553942b0f52c0c9b3f0584d1e9bef24bbaf5d6d386cf","target":"graph","created_at":"2026-07-05T06:14:21Z","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/2305.17104/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Prompt learning is a new paradigm for utilizing pre-trained language models and has achieved great success in many tasks. To adopt prompt learning in the NER task, two kinds of methods have been explored from a pair of symmetric perspectives, populating the template by enumerating spans to predict their entity types or constructing type-specific prompts to locate entities. However, these methods not only require a multi-round prompting manner with a high time overhead and computational cost, but also require elaborate prompt templates, that are difficult to apply in practical scenarios. In thi","authors_text":"Rongsheng Zhang, Shuhui Wu, Weiming Lu, Wenqi Zhang, Yadong Xi, Yongliang Shen, Yueting Zhuang, Zeqi Tan","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-05-26T17:16:11Z","title":"PromptNER: Prompt Locating and Typing for Named Entity Recognition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.17104","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:d0774d4e4917b52d979fde915fe039fb344fc19ee2f5d5a4cc12dadbc42630f7","target":"record","created_at":"2026-07-05T06:14:21Z","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":"bafc910d6e25c8e8b46de54f72ccf01cbe0eae0f922d57813b0c8ed0f4d9ab07","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-05-26T17:16:11Z","title_canon_sha256":"d4b1d434633fc4efbdc39f716cda0f5f6f715377156c9c7aed2637ab355cbb23"},"schema_version":"1.0","source":{"id":"2305.17104","kind":"arxiv","version":1}},"canonical_sha256":"33c42b204dcf9928c2082211a08dff60815960a5ecd35b6d7c13c9569918473e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"33c42b204dcf9928c2082211a08dff60815960a5ecd35b6d7c13c9569918473e","first_computed_at":"2026-07-05T06:14:21.271822Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:14:21.271822Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"WTKEoi9TB1uQx+9CIOOzq712qypUNASYHcxe5BbM0e65CQY08ES/HRHQrKnwpk8CMdxQAheuZPUj0eBt+UhZBg==","signature_status":"signed_v1","signed_at":"2026-07-05T06:14:21.272295Z","signed_message":"canonical_sha256_bytes"},"source_id":"2305.17104","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d0774d4e4917b52d979fde915fe039fb344fc19ee2f5d5a4cc12dadbc42630f7","sha256:5638d938fcff3943a69b553942b0f52c0c9b3f0584d1e9bef24bbaf5d6d386cf"],"state_sha256":"25bb4ebbfd2a506ab70ef145bc6b8baf455c52c7e44ce950e274ff1802604a52"}