{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:TREMDCVQAYRIHGZELNLXTIGNLD","short_pith_number":"pith:TREMDCVQ","schema_version":"1.0","canonical_sha256":"9c48c18ab00622839b245b5779a0cd58d8f3e28ebff0f7b2f14c5b72f3fbea84","source":{"kind":"arxiv","id":"2403.06158","version":1},"attestation_state":"computed","paper":{"title":"Are LLMs ready for Visualization?","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.HC","authors_text":"Pere-Pau V\\'azquez","submitted_at":"2024-03-10T10:09:34Z","abstract_excerpt":"Generative models have received a lot of attention in many areas of academia and the industry. Their capabilities span many areas, from the invention of images given a prompt to the generation of concrete code to solve a certain programming issue. These two paradigmatic cases fall within two distinct categories of requirements, ranging from \"creativity\" to \"precision\", as characterized by Bing Chat, which employs ChatGPT-4 as its backbone. Visualization practitioners and researchers have wondered to what end one of such systems could accomplish our work in a more efficient way. Several works i"},"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":"2403.06158","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.HC","submitted_at":"2024-03-10T10:09:34Z","cross_cats_sorted":[],"title_canon_sha256":"4884f310006c5d0df32423d7f523d3e380fa1128dfbccebdd220cd9218b71ac9","abstract_canon_sha256":"c111b4b34c05074ae0882098a5cc9c2370458fca4cb2193932d30a4ae9b6ad12"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:54:19.122011Z","signature_b64":"BJZjC2QfxmDmlkV4wPCbUKaf6dGR0IfpIpS5UAyIIAe4GlW5pPZ7tg16PzGruEzAxXAvhce8cNd/SJjevu0yAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9c48c18ab00622839b245b5779a0cd58d8f3e28ebff0f7b2f14c5b72f3fbea84","last_reissued_at":"2026-07-05T07:54:19.121580Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:54:19.121580Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Are LLMs ready for Visualization?","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.HC","authors_text":"Pere-Pau V\\'azquez","submitted_at":"2024-03-10T10:09:34Z","abstract_excerpt":"Generative models have received a lot of attention in many areas of academia and the industry. Their capabilities span many areas, from the invention of images given a prompt to the generation of concrete code to solve a certain programming issue. These two paradigmatic cases fall within two distinct categories of requirements, ranging from \"creativity\" to \"precision\", as characterized by Bing Chat, which employs ChatGPT-4 as its backbone. Visualization practitioners and researchers have wondered to what end one of such systems could accomplish our work in a more efficient way. Several works i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.06158","kind":"arxiv","version":1},"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/2403.06158/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":"2403.06158","created_at":"2026-07-05T07:54:19.121637+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.06158v1","created_at":"2026-07-05T07:54:19.121637+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.06158","created_at":"2026-07-05T07:54:19.121637+00:00"},{"alias_kind":"pith_short_12","alias_value":"TREMDCVQAYRI","created_at":"2026-07-05T07:54:19.121637+00:00"},{"alias_kind":"pith_short_16","alias_value":"TREMDCVQAYRIHGZE","created_at":"2026-07-05T07:54:19.121637+00:00"},{"alias_kind":"pith_short_8","alias_value":"TREMDCVQ","created_at":"2026-07-05T07:54:19.121637+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.02784","citing_title":"FathomGPT: A Natural Language Interface for Interactively Exploring Ocean Science Data","ref_index":51,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TREMDCVQAYRIHGZELNLXTIGNLD","json":"https://pith.science/pith/TREMDCVQAYRIHGZELNLXTIGNLD.json","graph_json":"https://pith.science/api/pith-number/TREMDCVQAYRIHGZELNLXTIGNLD/graph.json","events_json":"https://pith.science/api/pith-number/TREMDCVQAYRIHGZELNLXTIGNLD/events.json","paper":"https://pith.science/paper/TREMDCVQ"},"agent_actions":{"view_html":"https://pith.science/pith/TREMDCVQAYRIHGZELNLXTIGNLD","download_json":"https://pith.science/pith/TREMDCVQAYRIHGZELNLXTIGNLD.json","view_paper":"https://pith.science/paper/TREMDCVQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.06158&json=true","fetch_graph":"https://pith.science/api/pith-number/TREMDCVQAYRIHGZELNLXTIGNLD/graph.json","fetch_events":"https://pith.science/api/pith-number/TREMDCVQAYRIHGZELNLXTIGNLD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TREMDCVQAYRIHGZELNLXTIGNLD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TREMDCVQAYRIHGZELNLXTIGNLD/action/storage_attestation","attest_author":"https://pith.science/pith/TREMDCVQAYRIHGZELNLXTIGNLD/action/author_attestation","sign_citation":"https://pith.science/pith/TREMDCVQAYRIHGZELNLXTIGNLD/action/citation_signature","submit_replication":"https://pith.science/pith/TREMDCVQAYRIHGZELNLXTIGNLD/action/replication_record"}},"created_at":"2026-07-05T07:54:19.121637+00:00","updated_at":"2026-07-05T07:54:19.121637+00:00"}