{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:GKGXIX4TUFQYYVC4PYWN5L35IU","short_pith_number":"pith:GKGXIX4T","schema_version":"1.0","canonical_sha256":"328d745f93a1618c545c7e2cdeaf7d45273b7c3a145af47b3ee7b7da26aa16c8","source":{"kind":"arxiv","id":"2402.13823","version":3},"attestation_state":"computed","paper":{"title":"Using Large Language Models for Natural Language Processing Tasks in Requirements Engineering: A Systematic Guideline","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Andreas Vogelsang, Jannik Fischbach","submitted_at":"2024-02-21T14:00:52Z","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"},"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":"2402.13823","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2024-02-21T14:00:52Z","cross_cats_sorted":[],"title_canon_sha256":"a4e96361659ed1664f2c69db9923fa126c5d3e3796315fe65343c9dfc8c17658","abstract_canon_sha256":"bd9261313760d7739b335aa146f9f3e962fdac010f18be4d1628949966a45253"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:19:23.318112Z","signature_b64":"xkTJ6bVWEg6bga96Pfm6Fysx2OJ3g1xxwfLjJbofsaQEVXsQSkI0vGFfSaH7CAF54sAd9FrzpODjJUZDe9S/Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"328d745f93a1618c545c7e2cdeaf7d45273b7c3a145af47b3ee7b7da26aa16c8","last_reissued_at":"2026-07-05T08:19:23.317586Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:19:23.317586Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Using Large Language Models for Natural Language Processing Tasks in Requirements Engineering: A Systematic Guideline","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Andreas Vogelsang, Jannik Fischbach","submitted_at":"2024-02-21T14:00:52Z","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"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.13823","kind":"arxiv","version":3},"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/2402.13823/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":"2402.13823","created_at":"2026-07-05T08:19:23.317650+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.13823v3","created_at":"2026-07-05T08:19:23.317650+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.13823","created_at":"2026-07-05T08:19:23.317650+00:00"},{"alias_kind":"pith_short_12","alias_value":"GKGXIX4TUFQY","created_at":"2026-07-05T08:19:23.317650+00:00"},{"alias_kind":"pith_short_16","alias_value":"GKGXIX4TUFQYYVC4","created_at":"2026-07-05T08:19:23.317650+00:00"},{"alias_kind":"pith_short_8","alias_value":"GKGXIX4T","created_at":"2026-07-05T08:19:23.317650+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2607.00427","citing_title":"BT-APE: A Computationally Light Backtracking Approach to Automatic Prompt Engineering for Requirements Classification","ref_index":50,"is_internal_anchor":false},{"citing_arxiv_id":"2511.00262","citing_title":"LLM-Driven Cost-Effective Requirements Change Impact Analysis","ref_index":55,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GKGXIX4TUFQYYVC4PYWN5L35IU","json":"https://pith.science/pith/GKGXIX4TUFQYYVC4PYWN5L35IU.json","graph_json":"https://pith.science/api/pith-number/GKGXIX4TUFQYYVC4PYWN5L35IU/graph.json","events_json":"https://pith.science/api/pith-number/GKGXIX4TUFQYYVC4PYWN5L35IU/events.json","paper":"https://pith.science/paper/GKGXIX4T"},"agent_actions":{"view_html":"https://pith.science/pith/GKGXIX4TUFQYYVC4PYWN5L35IU","download_json":"https://pith.science/pith/GKGXIX4TUFQYYVC4PYWN5L35IU.json","view_paper":"https://pith.science/paper/GKGXIX4T","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.13823&json=true","fetch_graph":"https://pith.science/api/pith-number/GKGXIX4TUFQYYVC4PYWN5L35IU/graph.json","fetch_events":"https://pith.science/api/pith-number/GKGXIX4TUFQYYVC4PYWN5L35IU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GKGXIX4TUFQYYVC4PYWN5L35IU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GKGXIX4TUFQYYVC4PYWN5L35IU/action/storage_attestation","attest_author":"https://pith.science/pith/GKGXIX4TUFQYYVC4PYWN5L35IU/action/author_attestation","sign_citation":"https://pith.science/pith/GKGXIX4TUFQYYVC4PYWN5L35IU/action/citation_signature","submit_replication":"https://pith.science/pith/GKGXIX4TUFQYYVC4PYWN5L35IU/action/replication_record"}},"created_at":"2026-07-05T08:19:23.317650+00:00","updated_at":"2026-07-05T08:19:23.317650+00:00"}