{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:WW6XXKX6OYBDEIDEUL5I26ZNWG","short_pith_number":"pith:WW6XXKX6","canonical_record":{"source":{"id":"2403.09732","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-03-13T02:32:41Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"676369cf4581073ec3835218b8d12f95bb11c03e601562f2e7379ff86067d23e","abstract_canon_sha256":"19e37111c56a220f8358c941c148ae461a531dccdda0cca23eb130cee648f9e2"},"schema_version":"1.0"},"canonical_sha256":"b5bd7baafe7602322064a2fa8d7b2db1b772ee5e2590c0ad258acdb1573fd683","source":{"kind":"arxiv","id":"2403.09732","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.09732","created_at":"2026-07-05T08:26:21Z"},{"alias_kind":"arxiv_version","alias_value":"2403.09732v4","created_at":"2026-07-05T08:26:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.09732","created_at":"2026-07-05T08:26:21Z"},{"alias_kind":"pith_short_12","alias_value":"WW6XXKX6OYBD","created_at":"2026-07-05T08:26:21Z"},{"alias_kind":"pith_short_16","alias_value":"WW6XXKX6OYBDEIDE","created_at":"2026-07-05T08:26:21Z"},{"alias_kind":"pith_short_8","alias_value":"WW6XXKX6","created_at":"2026-07-05T08:26:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:WW6XXKX6OYBDEIDEUL5I26ZNWG","target":"record","payload":{"canonical_record":{"source":{"id":"2403.09732","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-03-13T02:32:41Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"676369cf4581073ec3835218b8d12f95bb11c03e601562f2e7379ff86067d23e","abstract_canon_sha256":"19e37111c56a220f8358c941c148ae461a531dccdda0cca23eb130cee648f9e2"},"schema_version":"1.0"},"canonical_sha256":"b5bd7baafe7602322064a2fa8d7b2db1b772ee5e2590c0ad258acdb1573fd683","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:26:21.747943Z","signature_b64":"nXntxEkuwmQImWG1Kf+tlNhFu/e00QVk8Gp5UgAgJMhg2/95+Q7bXyR7WrPsyyQUFi3H68zSgl1FnVBorgsRAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b5bd7baafe7602322064a2fa8d7b2db1b772ee5e2590c0ad258acdb1573fd683","last_reissued_at":"2026-07-05T08:26:21.747475Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:26:21.747475Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2403.09732","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:26:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cm9PaNT4cPcFWTasRsoTxHStX1ZSn49Ps3vNfyS9psOx3rM3t57TaQVOo4/wOlz2V9eWe8VW8mSpGPiHp0fbDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T07:54:47.430904Z"},"content_sha256":"dae2d8b80d250e355cc1504740048d3e193fb6bd7d62f438bd4459fd85ae8f26","schema_version":"1.0","event_id":"sha256:dae2d8b80d250e355cc1504740048d3e193fb6bd7d62f438bd4459fd85ae8f26"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:WW6XXKX6OYBDEIDEUL5I26ZNWG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"PET-SQL: A Prompt-Enhanced Two-Round Refinement of Text-to-SQL with Cross-consistency","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Bin Zhang, Guoqing Du, Hangyu Mao, Jingjing Zhao, Rui Zhao, Sun Yang, Xiang Wang, Xiaoru Hu, Yuxiao Ye, Zhishuai Li, Ziyue Li","submitted_at":"2024-03-13T02:32:41Z","abstract_excerpt":"Recent advancements in Text-to-SQL (Text2SQL) emphasize stimulating the large language models (LLM) on in-context learning, achieving significant results. Nevertheless, they face challenges when dealing with verbose database information and complex user intentions. This paper presents a two-stage framework to enhance the performance of current LLM-based natural language to SQL systems. We first introduce a novel prompt representation, called reference-enhanced representation, which includes schema information and randomly sampled cell values from tables to instruct LLMs in generating SQL queri"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.09732","kind":"arxiv","version":4},"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/2403.09732/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:26:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Yn/mo6TaLLdBnmpRcwPme/CSv0Odczs7Ak15tT1kO/g0cpKD27HuDo0bjUq+EUeN1/x8iC6v8tjUN2zZMMXNAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T07:54:47.431436Z"},"content_sha256":"44612e1fcb935037a475c34ca5fdddda50ad59bbf89b71b70efdf1bff6139a10","schema_version":"1.0","event_id":"sha256:44612e1fcb935037a475c34ca5fdddda50ad59bbf89b71b70efdf1bff6139a10"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WW6XXKX6OYBDEIDEUL5I26ZNWG/bundle.json","state_url":"https://pith.science/pith/WW6XXKX6OYBDEIDEUL5I26ZNWG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WW6XXKX6OYBDEIDEUL5I26ZNWG/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-09T07:54:47Z","links":{"resolver":"https://pith.science/pith/WW6XXKX6OYBDEIDEUL5I26ZNWG","bundle":"https://pith.science/pith/WW6XXKX6OYBDEIDEUL5I26ZNWG/bundle.json","state":"https://pith.science/pith/WW6XXKX6OYBDEIDEUL5I26ZNWG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WW6XXKX6OYBDEIDEUL5I26ZNWG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:WW6XXKX6OYBDEIDEUL5I26ZNWG","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"19e37111c56a220f8358c941c148ae461a531dccdda0cca23eb130cee648f9e2","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-03-13T02:32:41Z","title_canon_sha256":"676369cf4581073ec3835218b8d12f95bb11c03e601562f2e7379ff86067d23e"},"schema_version":"1.0","source":{"id":"2403.09732","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.09732","created_at":"2026-07-05T08:26:21Z"},{"alias_kind":"arxiv_version","alias_value":"2403.09732v4","created_at":"2026-07-05T08:26:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.09732","created_at":"2026-07-05T08:26:21Z"},{"alias_kind":"pith_short_12","alias_value":"WW6XXKX6OYBD","created_at":"2026-07-05T08:26:21Z"},{"alias_kind":"pith_short_16","alias_value":"WW6XXKX6OYBDEIDE","created_at":"2026-07-05T08:26:21Z"},{"alias_kind":"pith_short_8","alias_value":"WW6XXKX6","created_at":"2026-07-05T08:26:21Z"}],"graph_snapshots":[{"event_id":"sha256:44612e1fcb935037a475c34ca5fdddda50ad59bbf89b71b70efdf1bff6139a10","target":"graph","created_at":"2026-07-05T08:26:21Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2403.09732/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent advancements in Text-to-SQL (Text2SQL) emphasize stimulating the large language models (LLM) on in-context learning, achieving significant results. Nevertheless, they face challenges when dealing with verbose database information and complex user intentions. This paper presents a two-stage framework to enhance the performance of current LLM-based natural language to SQL systems. We first introduce a novel prompt representation, called reference-enhanced representation, which includes schema information and randomly sampled cell values from tables to instruct LLMs in generating SQL queri","authors_text":"Bin Zhang, Guoqing Du, Hangyu Mao, Jingjing Zhao, Rui Zhao, Sun Yang, Xiang Wang, Xiaoru Hu, Yuxiao Ye, Zhishuai Li, Ziyue Li","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-03-13T02:32:41Z","title":"PET-SQL: A Prompt-Enhanced Two-Round Refinement of Text-to-SQL with Cross-consistency"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.09732","kind":"arxiv","version":4},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:dae2d8b80d250e355cc1504740048d3e193fb6bd7d62f438bd4459fd85ae8f26","target":"record","created_at":"2026-07-05T08:26:21Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"19e37111c56a220f8358c941c148ae461a531dccdda0cca23eb130cee648f9e2","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-03-13T02:32:41Z","title_canon_sha256":"676369cf4581073ec3835218b8d12f95bb11c03e601562f2e7379ff86067d23e"},"schema_version":"1.0","source":{"id":"2403.09732","kind":"arxiv","version":4}},"canonical_sha256":"b5bd7baafe7602322064a2fa8d7b2db1b772ee5e2590c0ad258acdb1573fd683","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b5bd7baafe7602322064a2fa8d7b2db1b772ee5e2590c0ad258acdb1573fd683","first_computed_at":"2026-07-05T08:26:21.747475Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:26:21.747475Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"nXntxEkuwmQImWG1Kf+tlNhFu/e00QVk8Gp5UgAgJMhg2/95+Q7bXyR7WrPsyyQUFi3H68zSgl1FnVBorgsRAw==","signature_status":"signed_v1","signed_at":"2026-07-05T08:26:21.747943Z","signed_message":"canonical_sha256_bytes"},"source_id":"2403.09732","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:dae2d8b80d250e355cc1504740048d3e193fb6bd7d62f438bd4459fd85ae8f26","sha256:44612e1fcb935037a475c34ca5fdddda50ad59bbf89b71b70efdf1bff6139a10"],"state_sha256":"5be0c8d33cb3686b30e0d9b9a8ad0f56336001330bbc9603d8ae6d5baf874e59"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FjVWj29dCoDfuDd2nntl9DwxwIXBMVFENp87f54ZICpPkIIDN4xGt2ijM4wQysTsFZeP2NNTl9AFEI7y0tN5DQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T07:54:47.434684Z","bundle_sha256":"8f4aa670e413077f10b72d7cd4d4c17f9ed66d0e914632a2a90221573c5389b8"}}