{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:WUYAQV3TVRIBG7EXZ3MKD67PEH","short_pith_number":"pith:WUYAQV3T","schema_version":"1.0","canonical_sha256":"b530085773ac50137c97ced8a1fbef21f19f4d20c9d4e2896c10cbfe76aa391e","source":{"kind":"arxiv","id":"2305.11853","version":3},"attestation_state":"computed","paper":{"title":"How to Prompt LLMs for Text-to-SQL: A Study in Zero-shot, Single-domain, and Cross-domain Settings","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Eric Fosler-Lussier, Shuaichen Chang","submitted_at":"2023-05-19T17:43:58Z","abstract_excerpt":"Large language models (LLMs) with in-context learning have demonstrated remarkable capability in the text-to-SQL task. Previous research has prompted LLMs with various demonstration-retrieval strategies and intermediate reasoning steps to enhance the performance of LLMs. However, those works often employ varied strategies when constructing the prompt text for text-to-SQL inputs, such as databases and demonstration examples. This leads to a lack of comparability in both the prompt constructions and their primary contributions. Furthermore, selecting an effective prompt construction has emerged "},"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":"2305.11853","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-05-19T17:43:58Z","cross_cats_sorted":[],"title_canon_sha256":"f99e00187455a67bbd0f7104f2dde438ef5eaf589a5dc0598f2690116031c96f","abstract_canon_sha256":"24a0fbb0c11123a61e19abd5c1ac3f3fb169741e1e8cb35782b646aa7e9389b3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:16:41.525471Z","signature_b64":"Ry/ONpF/VGEGO2GnfMLfNFXkdf+EUwQaam3vBKH+/r+EpJ5B4Cy3kMncel7CICBq6bp5rECCwrkaOtI3YwteBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b530085773ac50137c97ced8a1fbef21f19f4d20c9d4e2896c10cbfe76aa391e","last_reissued_at":"2026-07-05T07:16:41.524983Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:16:41.524983Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"How to Prompt LLMs for Text-to-SQL: A Study in Zero-shot, Single-domain, and Cross-domain Settings","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Eric Fosler-Lussier, Shuaichen Chang","submitted_at":"2023-05-19T17:43:58Z","abstract_excerpt":"Large language models (LLMs) with in-context learning have demonstrated remarkable capability in the text-to-SQL task. Previous research has prompted LLMs with various demonstration-retrieval strategies and intermediate reasoning steps to enhance the performance of LLMs. However, those works often employ varied strategies when constructing the prompt text for text-to-SQL inputs, such as databases and demonstration examples. This leads to a lack of comparability in both the prompt constructions and their primary contributions. Furthermore, selecting an effective prompt construction has emerged "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.11853","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/2305.11853/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":"2305.11853","created_at":"2026-07-05T07:16:41.525041+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.11853v3","created_at":"2026-07-05T07:16:41.525041+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.11853","created_at":"2026-07-05T07:16:41.525041+00:00"},{"alias_kind":"pith_short_12","alias_value":"WUYAQV3TVRIB","created_at":"2026-07-05T07:16:41.525041+00:00"},{"alias_kind":"pith_short_16","alias_value":"WUYAQV3TVRIBG7EX","created_at":"2026-07-05T07:16:41.525041+00:00"},{"alias_kind":"pith_short_8","alias_value":"WUYAQV3T","created_at":"2026-07-05T07:16:41.525041+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.21414","citing_title":"SemanticAgent: A Semantics-Aware Framework for Text-to-SQL Data Synthesis","ref_index":30,"is_internal_anchor":false},{"citing_arxiv_id":"2409.12186","citing_title":"Qwen2.5-Coder Technical Report","ref_index":9,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WUYAQV3TVRIBG7EXZ3MKD67PEH","json":"https://pith.science/pith/WUYAQV3TVRIBG7EXZ3MKD67PEH.json","graph_json":"https://pith.science/api/pith-number/WUYAQV3TVRIBG7EXZ3MKD67PEH/graph.json","events_json":"https://pith.science/api/pith-number/WUYAQV3TVRIBG7EXZ3MKD67PEH/events.json","paper":"https://pith.science/paper/WUYAQV3T"},"agent_actions":{"view_html":"https://pith.science/pith/WUYAQV3TVRIBG7EXZ3MKD67PEH","download_json":"https://pith.science/pith/WUYAQV3TVRIBG7EXZ3MKD67PEH.json","view_paper":"https://pith.science/paper/WUYAQV3T","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.11853&json=true","fetch_graph":"https://pith.science/api/pith-number/WUYAQV3TVRIBG7EXZ3MKD67PEH/graph.json","fetch_events":"https://pith.science/api/pith-number/WUYAQV3TVRIBG7EXZ3MKD67PEH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WUYAQV3TVRIBG7EXZ3MKD67PEH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WUYAQV3TVRIBG7EXZ3MKD67PEH/action/storage_attestation","attest_author":"https://pith.science/pith/WUYAQV3TVRIBG7EXZ3MKD67PEH/action/author_attestation","sign_citation":"https://pith.science/pith/WUYAQV3TVRIBG7EXZ3MKD67PEH/action/citation_signature","submit_replication":"https://pith.science/pith/WUYAQV3TVRIBG7EXZ3MKD67PEH/action/replication_record"}},"created_at":"2026-07-05T07:16:41.525041+00:00","updated_at":"2026-07-05T07:16:41.525041+00:00"}