{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:Y2ODPKBS3IAFJHJ3H7DBIBJIET","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":"56dea5a62621ff815a1466a058c75dab69bc45a0e6b57bb6d72513ef9bf1a473","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-05-23T16:28:29Z","title_canon_sha256":"021e7de4377a27871ecfde5b71f51b3de9106a0533845d95355af2a7f6718afb"},"schema_version":"1.0","source":{"id":"2305.14210","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.14210","created_at":"2026-07-05T06:59:21Z"},{"alias_kind":"arxiv_version","alias_value":"2305.14210v2","created_at":"2026-07-05T06:59:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.14210","created_at":"2026-07-05T06:59:21Z"},{"alias_kind":"pith_short_12","alias_value":"Y2ODPKBS3IAF","created_at":"2026-07-05T06:59:21Z"},{"alias_kind":"pith_short_16","alias_value":"Y2ODPKBS3IAFJHJ3","created_at":"2026-07-05T06:59:21Z"},{"alias_kind":"pith_short_8","alias_value":"Y2ODPKBS","created_at":"2026-07-05T06:59:21Z"}],"graph_snapshots":[{"event_id":"sha256:6d91be1c7bfdc83f10ca3e27470b67f70bcf13600934b57f0ac4794215f7a183","target":"graph","created_at":"2026-07-05T06:59:21Z","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/2305.14210/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In-context learning is the paradigm that adapts large language models to downstream tasks by providing a few examples. Few-shot selection -- selecting appropriate examples for each test instance separately -- is important for in-context learning. In this paper, we propose Skill-KNN, a skill-based few-shot selection method for in-context learning. The key advantages of Skill-KNN include: (1) it addresses the problem that existing methods based on pre-trained embeddings can be easily biased by surface natural language features that are not important for the target task; (2) it does not require t","authors_text":"Bei Chen, Bo Zhou, Jian-Guang Lou, Nanning Zheng, Qiang Fu, Shengnan An, Weizhu Chen, Zeqi Lin","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-05-23T16:28:29Z","title":"Skill-Based Few-Shot Selection for In-Context Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.14210","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":"sha256:93934a37de661c570ae8159307ab5e96f1f9c27428aba359e4c586e85b65c625","target":"record","created_at":"2026-07-05T06:59:21Z","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":"56dea5a62621ff815a1466a058c75dab69bc45a0e6b57bb6d72513ef9bf1a473","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-05-23T16:28:29Z","title_canon_sha256":"021e7de4377a27871ecfde5b71f51b3de9106a0533845d95355af2a7f6718afb"},"schema_version":"1.0","source":{"id":"2305.14210","kind":"arxiv","version":2}},"canonical_sha256":"c69c37a832da00549d3b3fc614052824e2b022cf2665b3450c87393c8624f431","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c69c37a832da00549d3b3fc614052824e2b022cf2665b3450c87393c8624f431","first_computed_at":"2026-07-05T06:59:21.377713Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:59:21.377713Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"51btjhvDCCZN0VfJqOVZjqui+1sQt5RKllx3x1nOxufc+HKUIafTiZvUNe5Nxz9FX1I6Ow1nIKNnXWp+ESLbBg==","signature_status":"signed_v1","signed_at":"2026-07-05T06:59:21.378213Z","signed_message":"canonical_sha256_bytes"},"source_id":"2305.14210","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:93934a37de661c570ae8159307ab5e96f1f9c27428aba359e4c586e85b65c625","sha256:6d91be1c7bfdc83f10ca3e27470b67f70bcf13600934b57f0ac4794215f7a183"],"state_sha256":"1ef64e3e9464292fe63b3e8567366405d40e22bc73fa0dfb2ea813abaa0eb902"}