{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:2ZPKSHLOUULNRZEIARPYXJLE7L","short_pith_number":"pith:2ZPKSHLO","schema_version":"1.0","canonical_sha256":"d65ea91d6ea516d8e488045f8ba564faeb21570035371560c1f62411dce33a1d","source":{"kind":"arxiv","id":"2312.08642","version":2},"attestation_state":"computed","paper":{"title":"Metacognition-Enhanced Few-Shot Prompting With Positive Reinforcement","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Hong Zheng, Liang He, Wen Wu, Yi Hu, Yu Ji","submitted_at":"2023-12-14T03:49:52Z","abstract_excerpt":"Few-shot prompting elicits the remarkable abilities of large language models by equipping them with a few demonstration examples in the input. However, the traditional method of providing large language models with all demonstration input-output pairs at once may not effectively guide large language models to learn the specific input-output mapping relationship. In this paper, inspired by the regulatory and supportive role of metacognition in students' learning, we propose a novel metacognition-enhanced few-shot prompting, which guides large language models to reflect on their thought processe"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2312.08642","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-12-14T03:49:52Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"a5efd7c2c39c45122ce42fd5c8c35e1c0a7a678241cfc4a29a97400edf3a3075","abstract_canon_sha256":"87437e4ea7de85a27679c75f03268e491d5026a711cfa96af8af887f4bccd97e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:27:53.760357Z","signature_b64":"70ztU4DJmXLhVqr2LQIs49azfc9bk+c1BQtFYofAVlu2iyqiN3pu9Ek4XkGw2STwzc+Om98ckEsUulNoL0hcAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d65ea91d6ea516d8e488045f8ba564faeb21570035371560c1f62411dce33a1d","last_reissued_at":"2026-07-05T07:27:53.759792Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:27:53.759792Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Metacognition-Enhanced Few-Shot Prompting With Positive Reinforcement","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Hong Zheng, Liang He, Wen Wu, Yi Hu, Yu Ji","submitted_at":"2023-12-14T03:49:52Z","abstract_excerpt":"Few-shot prompting elicits the remarkable abilities of large language models by equipping them with a few demonstration examples in the input. However, the traditional method of providing large language models with all demonstration input-output pairs at once may not effectively guide large language models to learn the specific input-output mapping relationship. In this paper, inspired by the regulatory and supportive role of metacognition in students' learning, we propose a novel metacognition-enhanced few-shot prompting, which guides large language models to reflect on their thought processe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.08642","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/2312.08642/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2312.08642","created_at":"2026-07-05T07:27:53.759851+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.08642v2","created_at":"2026-07-05T07:27:53.759851+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.08642","created_at":"2026-07-05T07:27:53.759851+00:00"},{"alias_kind":"pith_short_12","alias_value":"2ZPKSHLOUULN","created_at":"2026-07-05T07:27:53.759851+00:00"},{"alias_kind":"pith_short_16","alias_value":"2ZPKSHLOUULNRZEI","created_at":"2026-07-05T07:27:53.759851+00:00"},{"alias_kind":"pith_short_8","alias_value":"2ZPKSHLO","created_at":"2026-07-05T07:27:53.759851+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2ZPKSHLOUULNRZEIARPYXJLE7L","json":"https://pith.science/pith/2ZPKSHLOUULNRZEIARPYXJLE7L.json","graph_json":"https://pith.science/api/pith-number/2ZPKSHLOUULNRZEIARPYXJLE7L/graph.json","events_json":"https://pith.science/api/pith-number/2ZPKSHLOUULNRZEIARPYXJLE7L/events.json","paper":"https://pith.science/paper/2ZPKSHLO"},"agent_actions":{"view_html":"https://pith.science/pith/2ZPKSHLOUULNRZEIARPYXJLE7L","download_json":"https://pith.science/pith/2ZPKSHLOUULNRZEIARPYXJLE7L.json","view_paper":"https://pith.science/paper/2ZPKSHLO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.08642&json=true","fetch_graph":"https://pith.science/api/pith-number/2ZPKSHLOUULNRZEIARPYXJLE7L/graph.json","fetch_events":"https://pith.science/api/pith-number/2ZPKSHLOUULNRZEIARPYXJLE7L/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2ZPKSHLOUULNRZEIARPYXJLE7L/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2ZPKSHLOUULNRZEIARPYXJLE7L/action/storage_attestation","attest_author":"https://pith.science/pith/2ZPKSHLOUULNRZEIARPYXJLE7L/action/author_attestation","sign_citation":"https://pith.science/pith/2ZPKSHLOUULNRZEIARPYXJLE7L/action/citation_signature","submit_replication":"https://pith.science/pith/2ZPKSHLOUULNRZEIARPYXJLE7L/action/replication_record"}},"created_at":"2026-07-05T07:27:53.759851+00:00","updated_at":"2026-07-05T07:27:53.759851+00:00"}