{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:ZAKR6WKBO5WTVXN6XBK4MFK2U7","short_pith_number":"pith:ZAKR6WKB","canonical_record":{"source":{"id":"2212.09420","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SE","submitted_at":"2022-12-19T12:55:32Z","cross_cats_sorted":["cs.AI","cs.CL","cs.PL"],"title_canon_sha256":"00f00ab897e12baa81f51ea2b0d4fd3c82643881fa0c4e118ec85ba81ec82b5d","abstract_canon_sha256":"62f335cb479182ff5bc941bb6ea2b1e0c328b365b1c3f8d780aea1d7ae255e8e"},"schema_version":"1.0"},"canonical_sha256":"c8151f5941776d3addbeb855c6155aa7e370c3d478bfda96c0ad5be48b35d16d","source":{"kind":"arxiv","id":"2212.09420","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2212.09420","created_at":"2026-07-05T06:07:35Z"},{"alias_kind":"arxiv_version","alias_value":"2212.09420v2","created_at":"2026-07-05T06:07:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.09420","created_at":"2026-07-05T06:07:35Z"},{"alias_kind":"pith_short_12","alias_value":"ZAKR6WKBO5WT","created_at":"2026-07-05T06:07:35Z"},{"alias_kind":"pith_short_16","alias_value":"ZAKR6WKBO5WTVXN6","created_at":"2026-07-05T06:07:35Z"},{"alias_kind":"pith_short_8","alias_value":"ZAKR6WKB","created_at":"2026-07-05T06:07:35Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:ZAKR6WKBO5WTVXN6XBK4MFK2U7","target":"record","payload":{"canonical_record":{"source":{"id":"2212.09420","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SE","submitted_at":"2022-12-19T12:55:32Z","cross_cats_sorted":["cs.AI","cs.CL","cs.PL"],"title_canon_sha256":"00f00ab897e12baa81f51ea2b0d4fd3c82643881fa0c4e118ec85ba81ec82b5d","abstract_canon_sha256":"62f335cb479182ff5bc941bb6ea2b1e0c328b365b1c3f8d780aea1d7ae255e8e"},"schema_version":"1.0"},"canonical_sha256":"c8151f5941776d3addbeb855c6155aa7e370c3d478bfda96c0ad5be48b35d16d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:07:35.387889Z","signature_b64":"pqIsGzO+HHHbyfC7R6LbNuBjtPWW8d6zpWA9+NvTMKZyY8NyNcwkW4hyMq83YS4Vf9wmykXA4mLd/RHEpjowCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c8151f5941776d3addbeb855c6155aa7e370c3d478bfda96c0ad5be48b35d16d","last_reissued_at":"2026-07-05T06:07:35.387214Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:07:35.387214Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2212.09420","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:07:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vlHzSOHZvDUvpnRATUbamF2UDweV/2w0zaLsI3F4yWSu8yOTkzFjWJfErOioiPbUEgft+c5KdFaA2/c3iB2zAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T22:04:58.339542Z"},"content_sha256":"f0a5a81e38f5edca16b1eafc1b56d55f108c9c22d6cad22127e1b302b2e937b0","schema_version":"1.0","event_id":"sha256:f0a5a81e38f5edca16b1eafc1b56d55f108c9c22d6cad22127e1b302b2e937b0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:ZAKR6WKBO5WTVXN6XBK4MFK2U7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Large Language Models Meet NL2Code: A Survey","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.PL"],"primary_cat":"cs.SE","authors_text":"Bei Chen, Bei Guan, Bingchao Wu, Daoguang Zan, Dianjie Lu, Fengji Zhang, Jian-Guang Lou, Yongji Wang","submitted_at":"2022-12-19T12:55:32Z","abstract_excerpt":"The task of generating code from a natural language description, or NL2Code, is considered a pressing and significant challenge in code intelligence. Thanks to the rapid development of pre-training techniques, surging large language models are being proposed for code, sparking the advances in NL2Code. To facilitate further research and applications in this field, in this paper, we present a comprehensive survey of 27 existing large language models for NL2Code, and also review benchmarks and metrics. We provide an intuitive comparison of all existing models on the HumanEval benchmark. Through i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.09420","kind":"arxiv","version":2},"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/2212.09420/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":"2026-07-05T06:07:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6sTw+WXBkeKsNe9UH71yNUcPMzo9JlQ2vOD3No8bzbbxO8crg4FXeGMuehFWyajvafJ4/UW9fFd041z0yebgCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T22:04:58.340054Z"},"content_sha256":"4d6b041a80ccddd755f8c9657f812faaf86e75aa9ee80d7e22c4859620c2a7ef","schema_version":"1.0","event_id":"sha256:4d6b041a80ccddd755f8c9657f812faaf86e75aa9ee80d7e22c4859620c2a7ef"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZAKR6WKBO5WTVXN6XBK4MFK2U7/bundle.json","state_url":"https://pith.science/pith/ZAKR6WKBO5WTVXN6XBK4MFK2U7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZAKR6WKBO5WTVXN6XBK4MFK2U7/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":"2026-08-20T22:04:58Z","links":{"resolver":"https://pith.science/pith/ZAKR6WKBO5WTVXN6XBK4MFK2U7","bundle":"https://pith.science/pith/ZAKR6WKBO5WTVXN6XBK4MFK2U7/bundle.json","state":"https://pith.science/pith/ZAKR6WKBO5WTVXN6XBK4MFK2U7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZAKR6WKBO5WTVXN6XBK4MFK2U7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:ZAKR6WKBO5WTVXN6XBK4MFK2U7","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":"62f335cb479182ff5bc941bb6ea2b1e0c328b365b1c3f8d780aea1d7ae255e8e","cross_cats_sorted":["cs.AI","cs.CL","cs.PL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SE","submitted_at":"2022-12-19T12:55:32Z","title_canon_sha256":"00f00ab897e12baa81f51ea2b0d4fd3c82643881fa0c4e118ec85ba81ec82b5d"},"schema_version":"1.0","source":{"id":"2212.09420","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2212.09420","created_at":"2026-07-05T06:07:35Z"},{"alias_kind":"arxiv_version","alias_value":"2212.09420v2","created_at":"2026-07-05T06:07:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.09420","created_at":"2026-07-05T06:07:35Z"},{"alias_kind":"pith_short_12","alias_value":"ZAKR6WKBO5WT","created_at":"2026-07-05T06:07:35Z"},{"alias_kind":"pith_short_16","alias_value":"ZAKR6WKBO5WTVXN6","created_at":"2026-07-05T06:07:35Z"},{"alias_kind":"pith_short_8","alias_value":"ZAKR6WKB","created_at":"2026-07-05T06:07:35Z"}],"graph_snapshots":[{"event_id":"sha256:4d6b041a80ccddd755f8c9657f812faaf86e75aa9ee80d7e22c4859620c2a7ef","target":"graph","created_at":"2026-07-05T06:07:35Z","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/2212.09420/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The task of generating code from a natural language description, or NL2Code, is considered a pressing and significant challenge in code intelligence. Thanks to the rapid development of pre-training techniques, surging large language models are being proposed for code, sparking the advances in NL2Code. To facilitate further research and applications in this field, in this paper, we present a comprehensive survey of 27 existing large language models for NL2Code, and also review benchmarks and metrics. We provide an intuitive comparison of all existing models on the HumanEval benchmark. Through i","authors_text":"Bei Chen, Bei Guan, Bingchao Wu, Daoguang Zan, Dianjie Lu, Fengji Zhang, Jian-Guang Lou, Yongji Wang","cross_cats":["cs.AI","cs.CL","cs.PL"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SE","submitted_at":"2022-12-19T12:55:32Z","title":"Large Language Models Meet NL2Code: A Survey"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.09420","kind":"arxiv","version":2},"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:f0a5a81e38f5edca16b1eafc1b56d55f108c9c22d6cad22127e1b302b2e937b0","target":"record","created_at":"2026-07-05T06:07:35Z","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":"62f335cb479182ff5bc941bb6ea2b1e0c328b365b1c3f8d780aea1d7ae255e8e","cross_cats_sorted":["cs.AI","cs.CL","cs.PL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SE","submitted_at":"2022-12-19T12:55:32Z","title_canon_sha256":"00f00ab897e12baa81f51ea2b0d4fd3c82643881fa0c4e118ec85ba81ec82b5d"},"schema_version":"1.0","source":{"id":"2212.09420","kind":"arxiv","version":2}},"canonical_sha256":"c8151f5941776d3addbeb855c6155aa7e370c3d478bfda96c0ad5be48b35d16d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c8151f5941776d3addbeb855c6155aa7e370c3d478bfda96c0ad5be48b35d16d","first_computed_at":"2026-07-05T06:07:35.387214Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:07:35.387214Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"pqIsGzO+HHHbyfC7R6LbNuBjtPWW8d6zpWA9+NvTMKZyY8NyNcwkW4hyMq83YS4Vf9wmykXA4mLd/RHEpjowCg==","signature_status":"signed_v1","signed_at":"2026-07-05T06:07:35.387889Z","signed_message":"canonical_sha256_bytes"},"source_id":"2212.09420","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f0a5a81e38f5edca16b1eafc1b56d55f108c9c22d6cad22127e1b302b2e937b0","sha256:4d6b041a80ccddd755f8c9657f812faaf86e75aa9ee80d7e22c4859620c2a7ef"],"state_sha256":"ebdd9e33ad18af593c546d2302300e2e41610dda3627cdaef2bcab8b167df203"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9xGRVnt6WEWm6uMps2DgWnIl/a6tDsZ4lgkYX+rmkwfVXhkiCIKTX+gK+6uDP3JiZq9VSgjvecVE9NafS3w8Dw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-20T22:04:58.343718Z","bundle_sha256":"b49a8cf313451249c837d3245c68c7f98b239c1f82b3f8bc02ab62fbd4998309"}}