{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:4N7HFPMBPQW3B7W6C5L4IGLKPP","short_pith_number":"pith:4N7HFPMB","schema_version":"1.0","canonical_sha256":"e37e72bd817c2db0fede1757c4196a7bfff86c1066520d03a55a82e07a75ae33","source":{"kind":"arxiv","id":"2305.12586","version":1},"attestation_state":"computed","paper":{"title":"Enhancing Few-shot Text-to-SQL Capabilities of Large Language Models: A Study on Prompt Design Strategies","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Arman Cohan, Dragomir Radev, Ellen Zhang, Jaesung Tae, Linyong Nan, Narutatsu Ri, Weijin Zou, Yilun Zhao","submitted_at":"2023-05-21T22:44:25Z","abstract_excerpt":"In-context learning (ICL) has emerged as a new approach to various natural language processing tasks, utilizing large language models (LLMs) to make predictions based on context that has been supplemented with a few examples or task-specific instructions. In this paper, we aim to extend this method to question answering tasks that utilize structured knowledge sources, and improve Text-to-SQL systems by exploring various prompt design strategies for employing LLMs. We conduct a systematic investigation into different demonstration selection methods and optimal instruction formats for prompting "},"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":"2305.12586","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2023-05-21T22:44:25Z","cross_cats_sorted":[],"title_canon_sha256":"1fdf3268a8240465d3f67f4e8be8a5a9755ad7873cd57b10b504f7ea593ba61f","abstract_canon_sha256":"0af439f952631b2df8d51e94797af0b67408eeb7228ac92a667626fc0a45d508"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:12:12.177659Z","signature_b64":"BOkiUZht4qU7ZRqgfM8UxJkPLtqcr49TJNXRLnzR+2yL68V68K9Uav4pnyocxMLKaRGYf71rhbRTLNc3M8OxCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e37e72bd817c2db0fede1757c4196a7bfff86c1066520d03a55a82e07a75ae33","last_reissued_at":"2026-07-05T06:12:12.177270Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:12:12.177270Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Enhancing Few-shot Text-to-SQL Capabilities of Large Language Models: A Study on Prompt Design Strategies","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Arman Cohan, Dragomir Radev, Ellen Zhang, Jaesung Tae, Linyong Nan, Narutatsu Ri, Weijin Zou, Yilun Zhao","submitted_at":"2023-05-21T22:44:25Z","abstract_excerpt":"In-context learning (ICL) has emerged as a new approach to various natural language processing tasks, utilizing large language models (LLMs) to make predictions based on context that has been supplemented with a few examples or task-specific instructions. In this paper, we aim to extend this method to question answering tasks that utilize structured knowledge sources, and improve Text-to-SQL systems by exploring various prompt design strategies for employing LLMs. We conduct a systematic investigation into different demonstration selection methods and optimal instruction formats for prompting "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.12586","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/2305.12586/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":"2305.12586","created_at":"2026-07-05T06:12:12.177333+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.12586v1","created_at":"2026-07-05T06:12:12.177333+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.12586","created_at":"2026-07-05T06:12:12.177333+00:00"},{"alias_kind":"pith_short_12","alias_value":"4N7HFPMBPQW3","created_at":"2026-07-05T06:12:12.177333+00:00"},{"alias_kind":"pith_short_16","alias_value":"4N7HFPMBPQW3B7W6","created_at":"2026-07-05T06:12:12.177333+00:00"},{"alias_kind":"pith_short_8","alias_value":"4N7HFPMB","created_at":"2026-07-05T06:12:12.177333+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.19988","citing_title":"Automatic Metadata Extraction for Text-to-SQL","ref_index":7,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4N7HFPMBPQW3B7W6C5L4IGLKPP","json":"https://pith.science/pith/4N7HFPMBPQW3B7W6C5L4IGLKPP.json","graph_json":"https://pith.science/api/pith-number/4N7HFPMBPQW3B7W6C5L4IGLKPP/graph.json","events_json":"https://pith.science/api/pith-number/4N7HFPMBPQW3B7W6C5L4IGLKPP/events.json","paper":"https://pith.science/paper/4N7HFPMB"},"agent_actions":{"view_html":"https://pith.science/pith/4N7HFPMBPQW3B7W6C5L4IGLKPP","download_json":"https://pith.science/pith/4N7HFPMBPQW3B7W6C5L4IGLKPP.json","view_paper":"https://pith.science/paper/4N7HFPMB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.12586&json=true","fetch_graph":"https://pith.science/api/pith-number/4N7HFPMBPQW3B7W6C5L4IGLKPP/graph.json","fetch_events":"https://pith.science/api/pith-number/4N7HFPMBPQW3B7W6C5L4IGLKPP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4N7HFPMBPQW3B7W6C5L4IGLKPP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4N7HFPMBPQW3B7W6C5L4IGLKPP/action/storage_attestation","attest_author":"https://pith.science/pith/4N7HFPMBPQW3B7W6C5L4IGLKPP/action/author_attestation","sign_citation":"https://pith.science/pith/4N7HFPMBPQW3B7W6C5L4IGLKPP/action/citation_signature","submit_replication":"https://pith.science/pith/4N7HFPMBPQW3B7W6C5L4IGLKPP/action/replication_record"}},"created_at":"2026-07-05T06:12:12.177333+00:00","updated_at":"2026-07-05T06:12:12.177333+00:00"}