{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:RVC5ZTFPFTKGSCZUMMWJM4WBUD","short_pith_number":"pith:RVC5ZTFP","schema_version":"1.0","canonical_sha256":"8d45dcccaf2cd4690b34632c9672c1a0f170008dceaf0995d825e880ed229f1a","source":{"kind":"arxiv","id":"2410.03446","version":1},"attestation_state":"computed","paper":{"title":"On Uncertainty In Natural Language Processing","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL","cs.LG"],"primary_cat":"cs.AI","authors_text":"Dennis Ulmer","submitted_at":"2024-10-04T14:08:02Z","abstract_excerpt":"The last decade in deep learning has brought on increasingly capable systems that are deployed on a wide variety of applications. In natural language processing, the field has been transformed by a number of breakthroughs including large language models, which are used in increasingly many user-facing applications. In order to reap the benefits of this technology and reduce potential harms, it is important to quantify the reliability of model predictions and the uncertainties that shroud their development.\n  This thesis studies how uncertainty in natural language processing can be characterize"},"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":"2410.03446","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-10-04T14:08:02Z","cross_cats_sorted":["cs.CL","cs.LG"],"title_canon_sha256":"4bc2dd5daa0d35447045987906a6f1e9a31cd4e7dcfc10dc27d46aa9e80c8979","abstract_canon_sha256":"c7dfa5ba944d752e397dd7f8fad01273747fb7f393aac3128ff12f5ee4e4e7f4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:23:33.637823Z","signature_b64":"JPra/IrazTXjeMKNpEc/2Cgr4OoM79ImHiwbvMtPctAxp+9kI3jOphR7PYO4ufcXZbD2GimGnIuYcSddfNp+DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8d45dcccaf2cd4690b34632c9672c1a0f170008dceaf0995d825e880ed229f1a","last_reissued_at":"2026-07-05T09:23:33.637222Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:23:33.637222Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"On Uncertainty In Natural Language Processing","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL","cs.LG"],"primary_cat":"cs.AI","authors_text":"Dennis Ulmer","submitted_at":"2024-10-04T14:08:02Z","abstract_excerpt":"The last decade in deep learning has brought on increasingly capable systems that are deployed on a wide variety of applications. In natural language processing, the field has been transformed by a number of breakthroughs including large language models, which are used in increasingly many user-facing applications. In order to reap the benefits of this technology and reduce potential harms, it is important to quantify the reliability of model predictions and the uncertainties that shroud their development.\n  This thesis studies how uncertainty in natural language processing can be characterize"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.03446","kind":"arxiv","version":1},"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/2410.03446/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":"2410.03446","created_at":"2026-07-05T09:23:33.637300+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.03446v1","created_at":"2026-07-05T09:23:33.637300+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.03446","created_at":"2026-07-05T09:23:33.637300+00:00"},{"alias_kind":"pith_short_12","alias_value":"RVC5ZTFPFTKG","created_at":"2026-07-05T09:23:33.637300+00:00"},{"alias_kind":"pith_short_16","alias_value":"RVC5ZTFPFTKGSCZU","created_at":"2026-07-05T09:23:33.637300+00:00"},{"alias_kind":"pith_short_8","alias_value":"RVC5ZTFP","created_at":"2026-07-05T09:23:33.637300+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.11128","citing_title":"What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests","ref_index":61,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RVC5ZTFPFTKGSCZUMMWJM4WBUD","json":"https://pith.science/pith/RVC5ZTFPFTKGSCZUMMWJM4WBUD.json","graph_json":"https://pith.science/api/pith-number/RVC5ZTFPFTKGSCZUMMWJM4WBUD/graph.json","events_json":"https://pith.science/api/pith-number/RVC5ZTFPFTKGSCZUMMWJM4WBUD/events.json","paper":"https://pith.science/paper/RVC5ZTFP"},"agent_actions":{"view_html":"https://pith.science/pith/RVC5ZTFPFTKGSCZUMMWJM4WBUD","download_json":"https://pith.science/pith/RVC5ZTFPFTKGSCZUMMWJM4WBUD.json","view_paper":"https://pith.science/paper/RVC5ZTFP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.03446&json=true","fetch_graph":"https://pith.science/api/pith-number/RVC5ZTFPFTKGSCZUMMWJM4WBUD/graph.json","fetch_events":"https://pith.science/api/pith-number/RVC5ZTFPFTKGSCZUMMWJM4WBUD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RVC5ZTFPFTKGSCZUMMWJM4WBUD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RVC5ZTFPFTKGSCZUMMWJM4WBUD/action/storage_attestation","attest_author":"https://pith.science/pith/RVC5ZTFPFTKGSCZUMMWJM4WBUD/action/author_attestation","sign_citation":"https://pith.science/pith/RVC5ZTFPFTKGSCZUMMWJM4WBUD/action/citation_signature","submit_replication":"https://pith.science/pith/RVC5ZTFPFTKGSCZUMMWJM4WBUD/action/replication_record"}},"created_at":"2026-07-05T09:23:33.637300+00:00","updated_at":"2026-07-05T09:23:33.637300+00:00"}