{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:QCYUAA7DKIVQPJBUJFBWCZCAV2","short_pith_number":"pith:QCYUAA7D","canonical_record":{"source":{"id":"2303.07142","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2023-03-13T14:09:53Z","cross_cats_sorted":[],"title_canon_sha256":"c06b08edfb2b5fef8c1c7cf9df92a8dfcfbbdda912965df28146831771400850","abstract_canon_sha256":"4ba5ddb09735bc9085ccdf95fae31b077951d58adaa7fdee639609a454d54efb"},"schema_version":"1.0"},"canonical_sha256":"80b14003e3522b07a4344943616440aeba5dc150ef1a08438263abcccea05fad","source":{"kind":"arxiv","id":"2303.07142","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2303.07142","created_at":"2026-07-05T06:02:08Z"},{"alias_kind":"arxiv_version","alias_value":"2303.07142v3","created_at":"2026-07-05T06:02:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.07142","created_at":"2026-07-05T06:02:08Z"},{"alias_kind":"pith_short_12","alias_value":"QCYUAA7DKIVQ","created_at":"2026-07-05T06:02:08Z"},{"alias_kind":"pith_short_16","alias_value":"QCYUAA7DKIVQPJBU","created_at":"2026-07-05T06:02:08Z"},{"alias_kind":"pith_short_8","alias_value":"QCYUAA7D","created_at":"2026-07-05T06:02:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:QCYUAA7DKIVQPJBUJFBWCZCAV2","target":"record","payload":{"canonical_record":{"source":{"id":"2303.07142","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2023-03-13T14:09:53Z","cross_cats_sorted":[],"title_canon_sha256":"c06b08edfb2b5fef8c1c7cf9df92a8dfcfbbdda912965df28146831771400850","abstract_canon_sha256":"4ba5ddb09735bc9085ccdf95fae31b077951d58adaa7fdee639609a454d54efb"},"schema_version":"1.0"},"canonical_sha256":"80b14003e3522b07a4344943616440aeba5dc150ef1a08438263abcccea05fad","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:02:08.077228Z","signature_b64":"vAjoZ2bEZHBhb3FscaMIrT9W/9GVHim6CKvtqzNVEfGWGoOhfo9Dw50K4i9tsQ0RW8oyo/SDeIv/el967/bKDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"80b14003e3522b07a4344943616440aeba5dc150ef1a08438263abcccea05fad","last_reissued_at":"2026-07-05T06:02:08.076699Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:02:08.076699Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2303.07142","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:02:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"c5gAbEP6AEs88VSu+4sFT5sXI0cNJfZAFD8Ei7fIOkxLWUiY+syQqoH0UIYhqgtRaJZchmSCREVD4LvCy7k5DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T20:10:46.027567Z"},"content_sha256":"0848080e1064a5d5e92375c3d8593b5c8132a1cd6b97dafe58da50aa240ee78c","schema_version":"1.0","event_id":"sha256:0848080e1064a5d5e92375c3d8593b5c8132a1cd6b97dafe58da50aa240ee78c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:QCYUAA7DKIVQPJBUJFBWCZCAV2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Large Language Models in the Workplace: A Case Study on Prompt Engineering for Job Type Classification","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Alexandru Ciceu, Benjamin Clavi\\'e, Frederick Naylor, Guillaume Souli\\'e, Thomas Brightwell","submitted_at":"2023-03-13T14:09:53Z","abstract_excerpt":"This case study investigates the task of job classification in a real-world setting, where the goal is to determine whether an English-language job posting is appropriate for a graduate or entry-level position. We explore multiple approaches to text classification, including supervised approaches such as traditional models like Support Vector Machines (SVMs) and state-of-the-art deep learning methods such as DeBERTa. We compare them with Large Language Models (LLMs) used in both few-shot and zero-shot classification settings. To accomplish this task, we employ prompt engineering, a technique t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.07142","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/2303.07142/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:02:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NYueP/STAjkXpqx8W9x2e65Jl45cICtypUzq+k6TnMJbpP3ITzS2nGonJyzKTXZOfy8MrfYZHDy7TIeNLY9iCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T20:10:46.028097Z"},"content_sha256":"f15a3d4e184c8dcde6c4065a3ff65c103427a3a1ac9baae9cf449f1857139b24","schema_version":"1.0","event_id":"sha256:f15a3d4e184c8dcde6c4065a3ff65c103427a3a1ac9baae9cf449f1857139b24"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QCYUAA7DKIVQPJBUJFBWCZCAV2/bundle.json","state_url":"https://pith.science/pith/QCYUAA7DKIVQPJBUJFBWCZCAV2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QCYUAA7DKIVQPJBUJFBWCZCAV2/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-09T20:10:46Z","links":{"resolver":"https://pith.science/pith/QCYUAA7DKIVQPJBUJFBWCZCAV2","bundle":"https://pith.science/pith/QCYUAA7DKIVQPJBUJFBWCZCAV2/bundle.json","state":"https://pith.science/pith/QCYUAA7DKIVQPJBUJFBWCZCAV2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QCYUAA7DKIVQPJBUJFBWCZCAV2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:QCYUAA7DKIVQPJBUJFBWCZCAV2","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":"4ba5ddb09735bc9085ccdf95fae31b077951d58adaa7fdee639609a454d54efb","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2023-03-13T14:09:53Z","title_canon_sha256":"c06b08edfb2b5fef8c1c7cf9df92a8dfcfbbdda912965df28146831771400850"},"schema_version":"1.0","source":{"id":"2303.07142","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2303.07142","created_at":"2026-07-05T06:02:08Z"},{"alias_kind":"arxiv_version","alias_value":"2303.07142v3","created_at":"2026-07-05T06:02:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.07142","created_at":"2026-07-05T06:02:08Z"},{"alias_kind":"pith_short_12","alias_value":"QCYUAA7DKIVQ","created_at":"2026-07-05T06:02:08Z"},{"alias_kind":"pith_short_16","alias_value":"QCYUAA7DKIVQPJBU","created_at":"2026-07-05T06:02:08Z"},{"alias_kind":"pith_short_8","alias_value":"QCYUAA7D","created_at":"2026-07-05T06:02:08Z"}],"graph_snapshots":[{"event_id":"sha256:f15a3d4e184c8dcde6c4065a3ff65c103427a3a1ac9baae9cf449f1857139b24","target":"graph","created_at":"2026-07-05T06:02:08Z","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/2303.07142/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This case study investigates the task of job classification in a real-world setting, where the goal is to determine whether an English-language job posting is appropriate for a graduate or entry-level position. We explore multiple approaches to text classification, including supervised approaches such as traditional models like Support Vector Machines (SVMs) and state-of-the-art deep learning methods such as DeBERTa. We compare them with Large Language Models (LLMs) used in both few-shot and zero-shot classification settings. To accomplish this task, we employ prompt engineering, a technique t","authors_text":"Alexandru Ciceu, Benjamin Clavi\\'e, Frederick Naylor, Guillaume Souli\\'e, Thomas Brightwell","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2023-03-13T14:09:53Z","title":"Large Language Models in the Workplace: A Case Study on Prompt Engineering for Job Type Classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.07142","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":"sha256:0848080e1064a5d5e92375c3d8593b5c8132a1cd6b97dafe58da50aa240ee78c","target":"record","created_at":"2026-07-05T06:02:08Z","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":"4ba5ddb09735bc9085ccdf95fae31b077951d58adaa7fdee639609a454d54efb","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2023-03-13T14:09:53Z","title_canon_sha256":"c06b08edfb2b5fef8c1c7cf9df92a8dfcfbbdda912965df28146831771400850"},"schema_version":"1.0","source":{"id":"2303.07142","kind":"arxiv","version":3}},"canonical_sha256":"80b14003e3522b07a4344943616440aeba5dc150ef1a08438263abcccea05fad","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"80b14003e3522b07a4344943616440aeba5dc150ef1a08438263abcccea05fad","first_computed_at":"2026-07-05T06:02:08.076699Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:02:08.076699Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"vAjoZ2bEZHBhb3FscaMIrT9W/9GVHim6CKvtqzNVEfGWGoOhfo9Dw50K4i9tsQ0RW8oyo/SDeIv/el967/bKDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T06:02:08.077228Z","signed_message":"canonical_sha256_bytes"},"source_id":"2303.07142","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0848080e1064a5d5e92375c3d8593b5c8132a1cd6b97dafe58da50aa240ee78c","sha256:f15a3d4e184c8dcde6c4065a3ff65c103427a3a1ac9baae9cf449f1857139b24"],"state_sha256":"214663a940eef100f559fa6f601a6dc927084aed6925c5df865b6d513d5f592b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lt6HQyIVAsn62BM5vBKs+UHKwO6H+bkkJLskP7zfYyA3uK79KzqW3vBeeHSPrSM2Fs2qHGD1T8vE9gB/DP49Bw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T20:10:46.033263Z","bundle_sha256":"9e979301d703213f67ba20c39c4003fa106a8187349ec74cda98b4eaec92485e"}}