{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:CBAJKLFF2AWFIRS4DNKRCKCZOZ","short_pith_number":"pith:CBAJKLFF","schema_version":"1.0","canonical_sha256":"1040952ca5d02c54465c1b55112859767b9a324b2cf7f5147dc3d126ffad4de7","source":{"kind":"arxiv","id":"2402.04955","version":2},"attestation_state":"computed","paper":{"title":"Conversational Assistants in Knowledge-Intensive Contexts: An Evaluation of LLM- versus Intent-based Systems","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.HC","authors_text":"Chaofan Wang, Evangelos Niforatos, Samuel Kernan Freire","submitted_at":"2024-02-07T15:39:07Z","abstract_excerpt":"Conversational Assistants (CA) are increasingly supporting human workers in knowledge management. Traditionally, CAs respond in specific ways to predefined user intents and conversation patterns. However, this rigidness does not handle the diversity of natural language well. Recent advances in natural language processing, namely Large Language Models (LLMs), enable CAs to converse in a more flexible, human-like manner, extracting relevant information from texts and capturing information from expert humans but introducing new challenges such as ``hallucinations''. To assess the potential of usi"},"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":"2402.04955","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.HC","submitted_at":"2024-02-07T15:39:07Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"f53a47e97bdc2e753b7853b1be6ec3da9e637ddbfe0634aaa5b519c3a8bba10e","abstract_canon_sha256":"2c3f0b00dfb9b501fda1f73f810acedb68b7770d7b459c1c60a6eba845ccab67"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:43:08.352991Z","signature_b64":"gjb+GZpxMI5hZzPJS/o8hNLDdkVS2amC/yX/LVBHWNzdkgGf7lTRAhjXqsS6TwLQA1x76IglOdRb6jnYkXZADA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1040952ca5d02c54465c1b55112859767b9a324b2cf7f5147dc3d126ffad4de7","last_reissued_at":"2026-07-05T08:43:08.352593Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:43:08.352593Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Conversational Assistants in Knowledge-Intensive Contexts: An Evaluation of LLM- versus Intent-based Systems","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.HC","authors_text":"Chaofan Wang, Evangelos Niforatos, Samuel Kernan Freire","submitted_at":"2024-02-07T15:39:07Z","abstract_excerpt":"Conversational Assistants (CA) are increasingly supporting human workers in knowledge management. Traditionally, CAs respond in specific ways to predefined user intents and conversation patterns. However, this rigidness does not handle the diversity of natural language well. Recent advances in natural language processing, namely Large Language Models (LLMs), enable CAs to converse in a more flexible, human-like manner, extracting relevant information from texts and capturing information from expert humans but introducing new challenges such as ``hallucinations''. To assess the potential of usi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.04955","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/2402.04955/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":"2402.04955","created_at":"2026-07-05T08:43:08.352650+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.04955v2","created_at":"2026-07-05T08:43:08.352650+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.04955","created_at":"2026-07-05T08:43:08.352650+00:00"},{"alias_kind":"pith_short_12","alias_value":"CBAJKLFF2AWF","created_at":"2026-07-05T08:43:08.352650+00:00"},{"alias_kind":"pith_short_16","alias_value":"CBAJKLFF2AWFIRS4","created_at":"2026-07-05T08:43:08.352650+00:00"},{"alias_kind":"pith_short_8","alias_value":"CBAJKLFF","created_at":"2026-07-05T08:43:08.352650+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.03807","citing_title":"Understanding Mental Models of Generative Conversational Search and The Effect of Interface Transparency","ref_index":16,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CBAJKLFF2AWFIRS4DNKRCKCZOZ","json":"https://pith.science/pith/CBAJKLFF2AWFIRS4DNKRCKCZOZ.json","graph_json":"https://pith.science/api/pith-number/CBAJKLFF2AWFIRS4DNKRCKCZOZ/graph.json","events_json":"https://pith.science/api/pith-number/CBAJKLFF2AWFIRS4DNKRCKCZOZ/events.json","paper":"https://pith.science/paper/CBAJKLFF"},"agent_actions":{"view_html":"https://pith.science/pith/CBAJKLFF2AWFIRS4DNKRCKCZOZ","download_json":"https://pith.science/pith/CBAJKLFF2AWFIRS4DNKRCKCZOZ.json","view_paper":"https://pith.science/paper/CBAJKLFF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.04955&json=true","fetch_graph":"https://pith.science/api/pith-number/CBAJKLFF2AWFIRS4DNKRCKCZOZ/graph.json","fetch_events":"https://pith.science/api/pith-number/CBAJKLFF2AWFIRS4DNKRCKCZOZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CBAJKLFF2AWFIRS4DNKRCKCZOZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CBAJKLFF2AWFIRS4DNKRCKCZOZ/action/storage_attestation","attest_author":"https://pith.science/pith/CBAJKLFF2AWFIRS4DNKRCKCZOZ/action/author_attestation","sign_citation":"https://pith.science/pith/CBAJKLFF2AWFIRS4DNKRCKCZOZ/action/citation_signature","submit_replication":"https://pith.science/pith/CBAJKLFF2AWFIRS4DNKRCKCZOZ/action/replication_record"}},"created_at":"2026-07-05T08:43:08.352650+00:00","updated_at":"2026-07-05T08:43:08.352650+00:00"}