{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:4FDHUIE5LSSZAGRBMDWFJ66EKL","short_pith_number":"pith:4FDHUIE5","schema_version":"1.0","canonical_sha256":"e1467a209d5ca5901a2160ec54fbc452e73a83968c7b495040e5d7c383e07874","source":{"kind":"arxiv","id":"2301.07507","version":1},"attestation_state":"computed","paper":{"title":"Graphix-T5: Mixing Pre-Trained Transformers with Graph-Aware Layers for Text-to-SQL Parsing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DB"],"primary_cat":"cs.CL","authors_text":"Binyuan Hui, Bowen Qin, Chenhao Ma, Fei Huang, Jinyang Li, Luo Si, Nan Huo, Reynold Cheng, Wenyu Du, Yongbin Li","submitted_at":"2023-01-18T13:29:05Z","abstract_excerpt":"The task of text-to-SQL parsing, which aims at converting natural language questions into executable SQL queries, has garnered increasing attention in recent years, as it can assist end users in efficiently extracting vital information from databases without the need for technical background. One of the major challenges in text-to-SQL parsing is domain generalization, i.e., how to generalize well to unseen databases. Recently, the pre-trained text-to-text transformer model, namely T5, though not specialized for text-to-SQL parsing, has achieved state-of-the-art performance on standard benchmar"},"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":"2301.07507","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-01-18T13:29:05Z","cross_cats_sorted":["cs.DB"],"title_canon_sha256":"31dddbad9e2de884311e4211e0741b1f9bbeb3c735fc33564b177d1d58b312a0","abstract_canon_sha256":"e3424838539fbb415a3cadd885f8fcdb68ddf8ddf954c89bf7c8a647574ca99f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:34:09.946915Z","signature_b64":"iKEJRIbi9nA8D2piipFGV17IyOvjgO0JrBXSmmAz5Y1ZHpxvBclJ0z/zHWzVqR+tGpbFg0bvFPMY3+/StBBJCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e1467a209d5ca5901a2160ec54fbc452e73a83968c7b495040e5d7c383e07874","last_reissued_at":"2026-07-05T05:34:09.946444Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:34:09.946444Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Graphix-T5: Mixing Pre-Trained Transformers with Graph-Aware Layers for Text-to-SQL Parsing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DB"],"primary_cat":"cs.CL","authors_text":"Binyuan Hui, Bowen Qin, Chenhao Ma, Fei Huang, Jinyang Li, Luo Si, Nan Huo, Reynold Cheng, Wenyu Du, Yongbin Li","submitted_at":"2023-01-18T13:29:05Z","abstract_excerpt":"The task of text-to-SQL parsing, which aims at converting natural language questions into executable SQL queries, has garnered increasing attention in recent years, as it can assist end users in efficiently extracting vital information from databases without the need for technical background. One of the major challenges in text-to-SQL parsing is domain generalization, i.e., how to generalize well to unseen databases. Recently, the pre-trained text-to-text transformer model, namely T5, though not specialized for text-to-SQL parsing, has achieved state-of-the-art performance on standard benchmar"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.07507","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/2301.07507/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":"2301.07507","created_at":"2026-07-05T05:34:09.946498+00:00"},{"alias_kind":"arxiv_version","alias_value":"2301.07507v1","created_at":"2026-07-05T05:34:09.946498+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.07507","created_at":"2026-07-05T05:34:09.946498+00:00"},{"alias_kind":"pith_short_12","alias_value":"4FDHUIE5LSSZ","created_at":"2026-07-05T05:34:09.946498+00:00"},{"alias_kind":"pith_short_16","alias_value":"4FDHUIE5LSSZAGRB","created_at":"2026-07-05T05:34:09.946498+00:00"},{"alias_kind":"pith_short_8","alias_value":"4FDHUIE5","created_at":"2026-07-05T05:34:09.946498+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2304.05128","citing_title":"Teaching Large Language Models to Self-Debug","ref_index":105,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4FDHUIE5LSSZAGRBMDWFJ66EKL","json":"https://pith.science/pith/4FDHUIE5LSSZAGRBMDWFJ66EKL.json","graph_json":"https://pith.science/api/pith-number/4FDHUIE5LSSZAGRBMDWFJ66EKL/graph.json","events_json":"https://pith.science/api/pith-number/4FDHUIE5LSSZAGRBMDWFJ66EKL/events.json","paper":"https://pith.science/paper/4FDHUIE5"},"agent_actions":{"view_html":"https://pith.science/pith/4FDHUIE5LSSZAGRBMDWFJ66EKL","download_json":"https://pith.science/pith/4FDHUIE5LSSZAGRBMDWFJ66EKL.json","view_paper":"https://pith.science/paper/4FDHUIE5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2301.07507&json=true","fetch_graph":"https://pith.science/api/pith-number/4FDHUIE5LSSZAGRBMDWFJ66EKL/graph.json","fetch_events":"https://pith.science/api/pith-number/4FDHUIE5LSSZAGRBMDWFJ66EKL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4FDHUIE5LSSZAGRBMDWFJ66EKL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4FDHUIE5LSSZAGRBMDWFJ66EKL/action/storage_attestation","attest_author":"https://pith.science/pith/4FDHUIE5LSSZAGRBMDWFJ66EKL/action/author_attestation","sign_citation":"https://pith.science/pith/4FDHUIE5LSSZAGRBMDWFJ66EKL/action/citation_signature","submit_replication":"https://pith.science/pith/4FDHUIE5LSSZAGRBMDWFJ66EKL/action/replication_record"}},"created_at":"2026-07-05T05:34:09.946498+00:00","updated_at":"2026-07-05T05:34:09.946498+00:00"}