{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:PJSA6RHBRDAXT4WE3DZMEJFFUV","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":"c7ff64d2b7d61f3c2df329cab627a3e4a6e3438546238688183e09720926dccc","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-10-10T14:16:01Z","title_canon_sha256":"284a4f8ada9d1d220f7c623271bccbc37aa2f67ec4b79f2c0781172eca9d040d"},"schema_version":"1.0","source":{"id":"2210.04714","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.04714","created_at":"2026-07-05T05:06:34Z"},{"alias_kind":"arxiv_version","alias_value":"2210.04714v2","created_at":"2026-07-05T05:06:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.04714","created_at":"2026-07-05T05:06:34Z"},{"alias_kind":"pith_short_12","alias_value":"PJSA6RHBRDAX","created_at":"2026-07-05T05:06:34Z"},{"alias_kind":"pith_short_16","alias_value":"PJSA6RHBRDAXT4WE","created_at":"2026-07-05T05:06:34Z"},{"alias_kind":"pith_short_8","alias_value":"PJSA6RHB","created_at":"2026-07-05T05:06:34Z"}],"graph_snapshots":[{"event_id":"sha256:9a488a82a64af10d0119e27a72b7f03878efaec0441370f9460b745fd2bba759","target":"graph","created_at":"2026-07-05T05:06:34Z","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/2210.04714/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Pre-trained language models (PLMs) have gained increasing popularity due to their compelling prediction performance in diverse natural language processing (NLP) tasks. When formulating a PLM-based prediction pipeline for NLP tasks, it is also crucial for the pipeline to minimize the calibration error, especially in safety-critical applications. That is, the pipeline should reliably indicate when we can trust its predictions. In particular, there are various considerations behind the pipeline: (1) the choice and (2) the size of PLM, (3) the choice of uncertainty quantifier, (4) the choice of fi","authors_text":"Louis-Philippe Morency, Paul Pu Liang, Ruslan Salakhutdinov, Umang Bhatt, Willie Neiswanger, Yuxin Xiao","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-10-10T14:16:01Z","title":"Uncertainty Quantification with Pre-trained Language Models: A Large-Scale Empirical Analysis"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.04714","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":"sha256:580e7bfd4e1f18e7fc6d9d48687038e4a8cd00d5a2fe184f03d5f59b9ad6ea4f","target":"record","created_at":"2026-07-05T05:06:34Z","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":"c7ff64d2b7d61f3c2df329cab627a3e4a6e3438546238688183e09720926dccc","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-10-10T14:16:01Z","title_canon_sha256":"284a4f8ada9d1d220f7c623271bccbc37aa2f67ec4b79f2c0781172eca9d040d"},"schema_version":"1.0","source":{"id":"2210.04714","kind":"arxiv","version":2}},"canonical_sha256":"7a640f44e188c179f2c4d8f2c224a5a577dea900944271958304c43f4e59b95b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7a640f44e188c179f2c4d8f2c224a5a577dea900944271958304c43f4e59b95b","first_computed_at":"2026-07-05T05:06:34.561042Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:06:34.561042Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"pAWpG5u/mZE81JFlWn04vAbH4jMukZSNKNgx5VCnyh7PcFKhQtEc0BCKb5aSqi7REseqQjgeGP+QsbRNQu3HCg==","signature_status":"signed_v1","signed_at":"2026-07-05T05:06:34.561510Z","signed_message":"canonical_sha256_bytes"},"source_id":"2210.04714","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:580e7bfd4e1f18e7fc6d9d48687038e4a8cd00d5a2fe184f03d5f59b9ad6ea4f","sha256:9a488a82a64af10d0119e27a72b7f03878efaec0441370f9460b745fd2bba759"],"state_sha256":"4abde1b13f594771f29b36b3442d32a1bda6f21033610a14355a7b4f75ee38eb"}