{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:BTJYCOJJK3ECB5AL26FTKTA4DL","short_pith_number":"pith:BTJYCOJJ","canonical_record":{"source":{"id":"2403.20014","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DB","submitted_at":"2024-03-29T07:01:29Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"1a0b00430039e9992eda3d8c59b7e07078058200512fbf8bc11fc1f44acaa217","abstract_canon_sha256":"eddc390b8323f8ca09943e17b8d75ccc034032ea5c8bd243dd295db08f916ca8"},"schema_version":"1.0"},"canonical_sha256":"0cd381392956c820f40bd78b354c1c1ac5ed68b884e03d2510a8795e8700821e","source":{"kind":"arxiv","id":"2403.20014","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.20014","created_at":"2026-07-05T08:02:12Z"},{"alias_kind":"arxiv_version","alias_value":"2403.20014v1","created_at":"2026-07-05T08:02:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.20014","created_at":"2026-07-05T08:02:12Z"},{"alias_kind":"pith_short_12","alias_value":"BTJYCOJJK3EC","created_at":"2026-07-05T08:02:12Z"},{"alias_kind":"pith_short_16","alias_value":"BTJYCOJJK3ECB5AL","created_at":"2026-07-05T08:02:12Z"},{"alias_kind":"pith_short_8","alias_value":"BTJYCOJJ","created_at":"2026-07-05T08:02:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:BTJYCOJJK3ECB5AL26FTKTA4DL","target":"record","payload":{"canonical_record":{"source":{"id":"2403.20014","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DB","submitted_at":"2024-03-29T07:01:29Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"1a0b00430039e9992eda3d8c59b7e07078058200512fbf8bc11fc1f44acaa217","abstract_canon_sha256":"eddc390b8323f8ca09943e17b8d75ccc034032ea5c8bd243dd295db08f916ca8"},"schema_version":"1.0"},"canonical_sha256":"0cd381392956c820f40bd78b354c1c1ac5ed68b884e03d2510a8795e8700821e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:02:12.079386Z","signature_b64":"GDOS17d7Ux/XTvp7KYymERVnkoLd9+M1qrD9rZ/Fy1KJ6NezlpYUTTcyf7y5g7FFPMFfj3rDDz1KCvTaOowVBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0cd381392956c820f40bd78b354c1c1ac5ed68b884e03d2510a8795e8700821e","last_reissued_at":"2026-07-05T08:02:12.078883Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:02:12.078883Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2403.20014","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:02:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Psbe4WVWSSlCyVcDcR60RkkrNYesxu7cT9OcO6SLnxLegZP6kdouCXssgO7eC6mNnOBkS0ZP+GPN/VDpL432Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T22:46:53.815709Z"},"content_sha256":"2aa81fd4f31af5895acf71ceea85272a9fb3ee59763d7cbc54f44af7b1f2f79e","schema_version":"1.0","event_id":"sha256:2aa81fd4f31af5895acf71ceea85272a9fb3ee59763d7cbc54f44af7b1f2f79e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:BTJYCOJJK3ECB5AL26FTKTA4DL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"PURPLE: Making a Large Language Model a Better SQL Writer","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.DB","authors_text":"Can Huang, Jiaqi Dai, Kai Zhang, Ren Huang, Tonghui Ren, X.Sean Wang, Yifan Yang, Yinan Jing, Yuankai Fan, Zhenying He","submitted_at":"2024-03-29T07:01:29Z","abstract_excerpt":"Large Language Model (LLM) techniques play an increasingly important role in Natural Language to SQL (NL2SQL) translation. LLMs trained by extensive corpora have strong natural language understanding and basic SQL generation abilities without additional tuning specific to NL2SQL tasks. Existing LLMs-based NL2SQL approaches try to improve the translation by enhancing the LLMs with an emphasis on user intention understanding. However, LLMs sometimes fail to generate appropriate SQL due to their lack of knowledge in organizing complex logical operator composition. A promising method is to input t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.20014","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/2403.20014/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":"2026-07-05T08:02:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3QI7NSXhvUP2NX1Zb9jzxp+L72cdpHFwB01IPFCoqxCIVa38J3mBrNHeo/eYW6ByAW9We0KNqIDS1xYWpOavDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T22:46:53.816112Z"},"content_sha256":"faca8e0f3b2ea73d4b8f6010567d577fb122d77398d8be1325bfc6039b2cf935","schema_version":"1.0","event_id":"sha256:faca8e0f3b2ea73d4b8f6010567d577fb122d77398d8be1325bfc6039b2cf935"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BTJYCOJJK3ECB5AL26FTKTA4DL/bundle.json","state_url":"https://pith.science/pith/BTJYCOJJK3ECB5AL26FTKTA4DL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BTJYCOJJK3ECB5AL26FTKTA4DL/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":"2026-08-13T22:46:53Z","links":{"resolver":"https://pith.science/pith/BTJYCOJJK3ECB5AL26FTKTA4DL","bundle":"https://pith.science/pith/BTJYCOJJK3ECB5AL26FTKTA4DL/bundle.json","state":"https://pith.science/pith/BTJYCOJJK3ECB5AL26FTKTA4DL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BTJYCOJJK3ECB5AL26FTKTA4DL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:BTJYCOJJK3ECB5AL26FTKTA4DL","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":"eddc390b8323f8ca09943e17b8d75ccc034032ea5c8bd243dd295db08f916ca8","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DB","submitted_at":"2024-03-29T07:01:29Z","title_canon_sha256":"1a0b00430039e9992eda3d8c59b7e07078058200512fbf8bc11fc1f44acaa217"},"schema_version":"1.0","source":{"id":"2403.20014","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.20014","created_at":"2026-07-05T08:02:12Z"},{"alias_kind":"arxiv_version","alias_value":"2403.20014v1","created_at":"2026-07-05T08:02:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.20014","created_at":"2026-07-05T08:02:12Z"},{"alias_kind":"pith_short_12","alias_value":"BTJYCOJJK3EC","created_at":"2026-07-05T08:02:12Z"},{"alias_kind":"pith_short_16","alias_value":"BTJYCOJJK3ECB5AL","created_at":"2026-07-05T08:02:12Z"},{"alias_kind":"pith_short_8","alias_value":"BTJYCOJJ","created_at":"2026-07-05T08:02:12Z"}],"graph_snapshots":[{"event_id":"sha256:faca8e0f3b2ea73d4b8f6010567d577fb122d77398d8be1325bfc6039b2cf935","target":"graph","created_at":"2026-07-05T08:02:12Z","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/2403.20014/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large Language Model (LLM) techniques play an increasingly important role in Natural Language to SQL (NL2SQL) translation. LLMs trained by extensive corpora have strong natural language understanding and basic SQL generation abilities without additional tuning specific to NL2SQL tasks. Existing LLMs-based NL2SQL approaches try to improve the translation by enhancing the LLMs with an emphasis on user intention understanding. However, LLMs sometimes fail to generate appropriate SQL due to their lack of knowledge in organizing complex logical operator composition. A promising method is to input t","authors_text":"Can Huang, Jiaqi Dai, Kai Zhang, Ren Huang, Tonghui Ren, X.Sean Wang, Yifan Yang, Yinan Jing, Yuankai Fan, Zhenying He","cross_cats":["cs.AI","cs.CL"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DB","submitted_at":"2024-03-29T07:01:29Z","title":"PURPLE: Making a Large Language Model a Better SQL Writer"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.20014","kind":"arxiv","version":1},"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":"sha256:2aa81fd4f31af5895acf71ceea85272a9fb3ee59763d7cbc54f44af7b1f2f79e","target":"record","created_at":"2026-07-05T08:02:12Z","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":"eddc390b8323f8ca09943e17b8d75ccc034032ea5c8bd243dd295db08f916ca8","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DB","submitted_at":"2024-03-29T07:01:29Z","title_canon_sha256":"1a0b00430039e9992eda3d8c59b7e07078058200512fbf8bc11fc1f44acaa217"},"schema_version":"1.0","source":{"id":"2403.20014","kind":"arxiv","version":1}},"canonical_sha256":"0cd381392956c820f40bd78b354c1c1ac5ed68b884e03d2510a8795e8700821e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0cd381392956c820f40bd78b354c1c1ac5ed68b884e03d2510a8795e8700821e","first_computed_at":"2026-07-05T08:02:12.078883Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:02:12.078883Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"GDOS17d7Ux/XTvp7KYymERVnkoLd9+M1qrD9rZ/Fy1KJ6NezlpYUTTcyf7y5g7FFPMFfj3rDDz1KCvTaOowVBw==","signature_status":"signed_v1","signed_at":"2026-07-05T08:02:12.079386Z","signed_message":"canonical_sha256_bytes"},"source_id":"2403.20014","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2aa81fd4f31af5895acf71ceea85272a9fb3ee59763d7cbc54f44af7b1f2f79e","sha256:faca8e0f3b2ea73d4b8f6010567d577fb122d77398d8be1325bfc6039b2cf935"],"state_sha256":"a7ef18968f8772e688c3fd6072175cc00c10ed48ede367a730febcf4a75fe8d8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QBXrCcGs5wXuXih+guH5QzOOVgXmn0yiffaWBoBvtemC79i12vcA/0RpanijYEXjWu+K3RnLPEkIRDX9LMNuAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-13T22:46:53.856924Z","bundle_sha256":"e07a58f4f233450abd2818d58ed34c64a1d1137ed26294abb52a335ba960dd65"}}