{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:7CLBF52STB74EYWSG2UWSE2YHK","short_pith_number":"pith:7CLBF52S","canonical_record":{"source":{"id":"2406.11434","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DB","submitted_at":"2024-06-17T11:40:41Z","cross_cats_sorted":[],"title_canon_sha256":"f72d5dea1b60aa7afdedefa8ebbe4f751f4daddabf6c5ca7c8ed22366181c81b","abstract_canon_sha256":"828c9f29a896557329a6358e17ff276c44b9e4d8c6d730d341988c46bbe183e1"},"schema_version":"1.0"},"canonical_sha256":"f89612f752987fc262d236a96913583aa8469642b1e211189d6162d43ad33c85","source":{"kind":"arxiv","id":"2406.11434","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.11434","created_at":"2026-07-05T08:32:53Z"},{"alias_kind":"arxiv_version","alias_value":"2406.11434v1","created_at":"2026-07-05T08:32:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.11434","created_at":"2026-07-05T08:32:53Z"},{"alias_kind":"pith_short_12","alias_value":"7CLBF52STB74","created_at":"2026-07-05T08:32:53Z"},{"alias_kind":"pith_short_16","alias_value":"7CLBF52STB74EYWS","created_at":"2026-07-05T08:32:53Z"},{"alias_kind":"pith_short_8","alias_value":"7CLBF52S","created_at":"2026-07-05T08:32:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:7CLBF52STB74EYWSG2UWSE2YHK","target":"record","payload":{"canonical_record":{"source":{"id":"2406.11434","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DB","submitted_at":"2024-06-17T11:40:41Z","cross_cats_sorted":[],"title_canon_sha256":"f72d5dea1b60aa7afdedefa8ebbe4f751f4daddabf6c5ca7c8ed22366181c81b","abstract_canon_sha256":"828c9f29a896557329a6358e17ff276c44b9e4d8c6d730d341988c46bbe183e1"},"schema_version":"1.0"},"canonical_sha256":"f89612f752987fc262d236a96913583aa8469642b1e211189d6162d43ad33c85","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:32:53.332484Z","signature_b64":"i7MSuMD7vGapafQAjTVDAighde8MgYrwY5Agnbtufmnvkx3Oak0jUcy88Z0A/KRfwIID/BCl5iM/iCAH4CcPBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f89612f752987fc262d236a96913583aa8469642b1e211189d6162d43ad33c85","last_reissued_at":"2026-07-05T08:32:53.331977Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:32:53.331977Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2406.11434","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":"2026-07-05T08:32:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8kGQ3H+SWD+3NuFIDz+gppKgVExGYu2eekHRTsLHzjk5kjOCuh0SPC6wbrVBT0sbdHDjUwGSCClDBN4J8zerAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T13:44:30.641834Z"},"content_sha256":"4abe5cf0ebb66821c40ce36c0cea4d4f8f68c0a948fdc2a7409ee6a07613acae","schema_version":"1.0","event_id":"sha256:4abe5cf0ebb66821c40ce36c0cea4d4f8f68c0a948fdc2a7409ee6a07613acae"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:7CLBF52STB74EYWSG2UWSE2YHK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"DB-GPT-Hub: Towards Open Benchmarking Text-to-SQL Empowered by Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DB","authors_text":"Caigai Jiang, Danrui Qi, Fan Zhou, Faqiang Chen, Ganglin Wei, Gangwei Jiang, Hongyang Zhang, Siqiao Xue, Wang Zhao, Wenhui Shi, Zhixuan Chu","submitted_at":"2024-06-17T11:40:41Z","abstract_excerpt":"Large language models (LLMs) becomes the dominant paradigm for the challenging task of text-to-SQL. LLM-empowered text-to-SQL methods are typically categorized into prompting-based and tuning approaches. Compared to prompting-based methods, benchmarking fine-tuned LLMs for text-to-SQL is important yet under-explored, partially attributed to the prohibitively high computational cost. In this paper, we present DB-GPT-Hub, an open benchmark suite for LLM-empowered text-to-SQL, which primarily focuses on tuning LLMs at large scales. The proposed benchmark consists of: 1. a standardized and compreh"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.11434","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/2406.11434/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":"2026-07-05T08:32:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"h4RCIrC3yTe+SQiyXbzxRUwbzflRtokx8LZuaFz0/XB2/Ta7RKBIQQAWYQR0eZGQ8TBUaLuy8NIgcpw0dyzRCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T13:44:30.642327Z"},"content_sha256":"2d4d38c1204f6d56c2f89754ac2eab9d716602e88d556b5c77e892e9df9396c7","schema_version":"1.0","event_id":"sha256:2d4d38c1204f6d56c2f89754ac2eab9d716602e88d556b5c77e892e9df9396c7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7CLBF52STB74EYWSG2UWSE2YHK/bundle.json","state_url":"https://pith.science/pith/7CLBF52STB74EYWSG2UWSE2YHK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7CLBF52STB74EYWSG2UWSE2YHK/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":"2026-08-05T13:44:30Z","links":{"resolver":"https://pith.science/pith/7CLBF52STB74EYWSG2UWSE2YHK","bundle":"https://pith.science/pith/7CLBF52STB74EYWSG2UWSE2YHK/bundle.json","state":"https://pith.science/pith/7CLBF52STB74EYWSG2UWSE2YHK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7CLBF52STB74EYWSG2UWSE2YHK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:7CLBF52STB74EYWSG2UWSE2YHK","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":"828c9f29a896557329a6358e17ff276c44b9e4d8c6d730d341988c46bbe183e1","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DB","submitted_at":"2024-06-17T11:40:41Z","title_canon_sha256":"f72d5dea1b60aa7afdedefa8ebbe4f751f4daddabf6c5ca7c8ed22366181c81b"},"schema_version":"1.0","source":{"id":"2406.11434","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.11434","created_at":"2026-07-05T08:32:53Z"},{"alias_kind":"arxiv_version","alias_value":"2406.11434v1","created_at":"2026-07-05T08:32:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.11434","created_at":"2026-07-05T08:32:53Z"},{"alias_kind":"pith_short_12","alias_value":"7CLBF52STB74","created_at":"2026-07-05T08:32:53Z"},{"alias_kind":"pith_short_16","alias_value":"7CLBF52STB74EYWS","created_at":"2026-07-05T08:32:53Z"},{"alias_kind":"pith_short_8","alias_value":"7CLBF52S","created_at":"2026-07-05T08:32:53Z"}],"graph_snapshots":[{"event_id":"sha256:2d4d38c1204f6d56c2f89754ac2eab9d716602e88d556b5c77e892e9df9396c7","target":"graph","created_at":"2026-07-05T08:32:53Z","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/2406.11434/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large language models (LLMs) becomes the dominant paradigm for the challenging task of text-to-SQL. LLM-empowered text-to-SQL methods are typically categorized into prompting-based and tuning approaches. Compared to prompting-based methods, benchmarking fine-tuned LLMs for text-to-SQL is important yet under-explored, partially attributed to the prohibitively high computational cost. In this paper, we present DB-GPT-Hub, an open benchmark suite for LLM-empowered text-to-SQL, which primarily focuses on tuning LLMs at large scales. The proposed benchmark consists of: 1. a standardized and compreh","authors_text":"Caigai Jiang, Danrui Qi, Fan Zhou, Faqiang Chen, Ganglin Wei, Gangwei Jiang, Hongyang Zhang, Siqiao Xue, Wang Zhao, Wenhui Shi, Zhixuan Chu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DB","submitted_at":"2024-06-17T11:40:41Z","title":"DB-GPT-Hub: Towards Open Benchmarking Text-to-SQL Empowered by Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.11434","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":"sha256:4abe5cf0ebb66821c40ce36c0cea4d4f8f68c0a948fdc2a7409ee6a07613acae","target":"record","created_at":"2026-07-05T08:32:53Z","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":"828c9f29a896557329a6358e17ff276c44b9e4d8c6d730d341988c46bbe183e1","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DB","submitted_at":"2024-06-17T11:40:41Z","title_canon_sha256":"f72d5dea1b60aa7afdedefa8ebbe4f751f4daddabf6c5ca7c8ed22366181c81b"},"schema_version":"1.0","source":{"id":"2406.11434","kind":"arxiv","version":1}},"canonical_sha256":"f89612f752987fc262d236a96913583aa8469642b1e211189d6162d43ad33c85","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f89612f752987fc262d236a96913583aa8469642b1e211189d6162d43ad33c85","first_computed_at":"2026-07-05T08:32:53.331977Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:32:53.331977Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"i7MSuMD7vGapafQAjTVDAighde8MgYrwY5Agnbtufmnvkx3Oak0jUcy88Z0A/KRfwIID/BCl5iM/iCAH4CcPBw==","signature_status":"signed_v1","signed_at":"2026-07-05T08:32:53.332484Z","signed_message":"canonical_sha256_bytes"},"source_id":"2406.11434","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4abe5cf0ebb66821c40ce36c0cea4d4f8f68c0a948fdc2a7409ee6a07613acae","sha256:2d4d38c1204f6d56c2f89754ac2eab9d716602e88d556b5c77e892e9df9396c7"],"state_sha256":"0705265d17c53fefc3c06dc5c0a3e8b13c1471bf21c45a9f8606c71ee6a32c77"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Gon3GS3wUolbQTLUhDbYN5XGAyNr2K71x+nSAyGtHYd33mjsYCw7PBMZD48DGb6JaT1bpr2AsfAgAJZ6c1M9Cw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T13:44:30.646097Z","bundle_sha256":"dcb7d9db7f2069789264ff330007301e95f68d48e1edeba99a1114389d5972ae"}}