{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:UVJVSUNEQVSVG3FSPGNKPSOEHR","short_pith_number":"pith:UVJVSUNE","schema_version":"1.0","canonical_sha256":"a5535951a48565536cb2799aa7c9c43c6c0fcc40edf0a37545e3faa34ae94caf","source":{"kind":"arxiv","id":"2404.17975","version":2},"attestation_state":"computed","paper":{"title":"Automating Customer Needs Analysis: A Comparative Study of Large Language Models in the Travel Industry","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.HC","cs.LG"],"primary_cat":"cs.CL","authors_text":"Emiliano Marrale, Filippo Chiarello, Lorenzo Cascone, Salvatore Puccio, Simone Barandoni","submitted_at":"2024-04-27T18:28:10Z","abstract_excerpt":"In the rapidly evolving landscape of Natural Language Processing (NLP), Large Language Models (LLMs) have emerged as powerful tools for many tasks, such as extracting valuable insights from vast amounts of textual data. In this study, we conduct a comparative analysis of LLMs for the extraction of travel customer needs from TripAdvisor and Reddit posts. Leveraging a diverse range of models, including both open-source and proprietary ones such as GPT-4 and Gemini, we aim to elucidate their strengths and weaknesses in this specialized domain. Through an evaluation process involving metrics such "},"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":"2404.17975","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-04-27T18:28:10Z","cross_cats_sorted":["cs.AI","cs.HC","cs.LG"],"title_canon_sha256":"cd78be69e211a5281a51d795e5a8c76cdcb76ecf68068aa31af78c8ae653f642","abstract_canon_sha256":"1ac73f052631b6c89fbdca39790fbf02872089fe4c88db4fa2ee8a27225f783c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:46:37.064776Z","signature_b64":"sJu+odslt8H+cFiTUx5zaLiLy9oZlF1YoHkOLV5w7vtwTTwOhb+ZCwf4CdqCOC+6BMurU4Y7uDcJpK10tfRoCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a5535951a48565536cb2799aa7c9c43c6c0fcc40edf0a37545e3faa34ae94caf","last_reissued_at":"2026-07-05T10:46:37.064214Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:46:37.064214Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Automating Customer Needs Analysis: A Comparative Study of Large Language Models in the Travel Industry","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.HC","cs.LG"],"primary_cat":"cs.CL","authors_text":"Emiliano Marrale, Filippo Chiarello, Lorenzo Cascone, Salvatore Puccio, Simone Barandoni","submitted_at":"2024-04-27T18:28:10Z","abstract_excerpt":"In the rapidly evolving landscape of Natural Language Processing (NLP), Large Language Models (LLMs) have emerged as powerful tools for many tasks, such as extracting valuable insights from vast amounts of textual data. In this study, we conduct a comparative analysis of LLMs for the extraction of travel customer needs from TripAdvisor and Reddit posts. Leveraging a diverse range of models, including both open-source and proprietary ones such as GPT-4 and Gemini, we aim to elucidate their strengths and weaknesses in this specialized domain. Through an evaluation process involving metrics such "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.17975","kind":"arxiv","version":2},"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/2404.17975/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":"2404.17975","created_at":"2026-07-05T10:46:37.064273+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.17975v2","created_at":"2026-07-05T10:46:37.064273+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.17975","created_at":"2026-07-05T10:46:37.064273+00:00"},{"alias_kind":"pith_short_12","alias_value":"UVJVSUNEQVSV","created_at":"2026-07-05T10:46:37.064273+00:00"},{"alias_kind":"pith_short_16","alias_value":"UVJVSUNEQVSVG3FS","created_at":"2026-07-05T10:46:37.064273+00:00"},{"alias_kind":"pith_short_8","alias_value":"UVJVSUNE","created_at":"2026-07-05T10:46:37.064273+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.17156","citing_title":"PersonaBOT: Bringing Customer Personas to Life with LLMs and RAG","ref_index":9,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UVJVSUNEQVSVG3FSPGNKPSOEHR","json":"https://pith.science/pith/UVJVSUNEQVSVG3FSPGNKPSOEHR.json","graph_json":"https://pith.science/api/pith-number/UVJVSUNEQVSVG3FSPGNKPSOEHR/graph.json","events_json":"https://pith.science/api/pith-number/UVJVSUNEQVSVG3FSPGNKPSOEHR/events.json","paper":"https://pith.science/paper/UVJVSUNE"},"agent_actions":{"view_html":"https://pith.science/pith/UVJVSUNEQVSVG3FSPGNKPSOEHR","download_json":"https://pith.science/pith/UVJVSUNEQVSVG3FSPGNKPSOEHR.json","view_paper":"https://pith.science/paper/UVJVSUNE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.17975&json=true","fetch_graph":"https://pith.science/api/pith-number/UVJVSUNEQVSVG3FSPGNKPSOEHR/graph.json","fetch_events":"https://pith.science/api/pith-number/UVJVSUNEQVSVG3FSPGNKPSOEHR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UVJVSUNEQVSVG3FSPGNKPSOEHR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UVJVSUNEQVSVG3FSPGNKPSOEHR/action/storage_attestation","attest_author":"https://pith.science/pith/UVJVSUNEQVSVG3FSPGNKPSOEHR/action/author_attestation","sign_citation":"https://pith.science/pith/UVJVSUNEQVSVG3FSPGNKPSOEHR/action/citation_signature","submit_replication":"https://pith.science/pith/UVJVSUNEQVSVG3FSPGNKPSOEHR/action/replication_record"}},"created_at":"2026-07-05T10:46:37.064273+00:00","updated_at":"2026-07-05T10:46:37.064273+00:00"}