{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:GKGXIX4TUFQYYVC4PYWN5L35IU","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":"bd9261313760d7739b335aa146f9f3e962fdac010f18be4d1628949966a45253","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2024-02-21T14:00:52Z","title_canon_sha256":"a4e96361659ed1664f2c69db9923fa126c5d3e3796315fe65343c9dfc8c17658"},"schema_version":"1.0","source":{"id":"2402.13823","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.13823","created_at":"2026-07-05T08:19:23Z"},{"alias_kind":"arxiv_version","alias_value":"2402.13823v3","created_at":"2026-07-05T08:19:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.13823","created_at":"2026-07-05T08:19:23Z"},{"alias_kind":"pith_short_12","alias_value":"GKGXIX4TUFQY","created_at":"2026-07-05T08:19:23Z"},{"alias_kind":"pith_short_16","alias_value":"GKGXIX4TUFQYYVC4","created_at":"2026-07-05T08:19:23Z"},{"alias_kind":"pith_short_8","alias_value":"GKGXIX4T","created_at":"2026-07-05T08:19:23Z"}],"graph_snapshots":[{"event_id":"sha256:a3d84888087212fcf14762a0b86b1b52dc7f75f1b6f5c064dd24c16e13c4cc16","target":"graph","created_at":"2026-07-05T08:19:23Z","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/2402.13823/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large Language Models (LLMs) are the cornerstone in automating Requirements Engineering (RE) tasks, underpinning recent advancements in the field. Their pre-trained comprehension of natural language is pivotal for effectively tailoring them to specific RE tasks. However, selecting an appropriate LLM from a myriad of existing architectures and fine-tuning it to address the intricacies of a given task poses a significant challenge for researchers and practitioners in the RE domain. Utilizing LLMs effectively for NLP problems in RE necessitates a dual understanding: firstly, of the inner workings","authors_text":"Andreas Vogelsang, Jannik Fischbach","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2024-02-21T14:00:52Z","title":"Using Large Language Models for Natural Language Processing Tasks in Requirements Engineering: A Systematic Guideline"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.13823","kind":"arxiv","version":3},"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":"sha256:0820bb267473cfed6c9f3e9585fafd22c06b23a9183a99da1c98bacc3177d3ea","target":"record","created_at":"2026-07-05T08:19:23Z","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":"bd9261313760d7739b335aa146f9f3e962fdac010f18be4d1628949966a45253","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2024-02-21T14:00:52Z","title_canon_sha256":"a4e96361659ed1664f2c69db9923fa126c5d3e3796315fe65343c9dfc8c17658"},"schema_version":"1.0","source":{"id":"2402.13823","kind":"arxiv","version":3}},"canonical_sha256":"328d745f93a1618c545c7e2cdeaf7d45273b7c3a145af47b3ee7b7da26aa16c8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"328d745f93a1618c545c7e2cdeaf7d45273b7c3a145af47b3ee7b7da26aa16c8","first_computed_at":"2026-07-05T08:19:23.317586Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:19:23.317586Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"xkTJ6bVWEg6bga96Pfm6Fysx2OJ3g1xxwfLjJbofsaQEVXsQSkI0vGFfSaH7CAF54sAd9FrzpODjJUZDe9S/Bw==","signature_status":"signed_v1","signed_at":"2026-07-05T08:19:23.318112Z","signed_message":"canonical_sha256_bytes"},"source_id":"2402.13823","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0820bb267473cfed6c9f3e9585fafd22c06b23a9183a99da1c98bacc3177d3ea","sha256:a3d84888087212fcf14762a0b86b1b52dc7f75f1b6f5c064dd24c16e13c4cc16"],"state_sha256":"d7522c7739655a846ba19da94f816bb0fb703fa129d95e44043a05eab2d229a3"}