{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:RMBA3XR4LNESDTSOMUW7DX4UA2","short_pith_number":"pith:RMBA3XR4","schema_version":"1.0","canonical_sha256":"8b020dde3c5b4921ce4e652df1df94069c3e0d65728060a9a3b88eadd51da8ca","source":{"kind":"arxiv","id":"2212.09666","version":1},"attestation_state":"computed","paper":{"title":"MultiCoder: Multi-Programming-Lingual Pre-Training for Low-Resource Code Completion","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Cuiyun Gao, Pingyi Zhou, Yasheng Wang, Yinpeng Guo, Zenglin Xu, Zi Gong","submitted_at":"2022-12-19T17:50:05Z","abstract_excerpt":"Code completion is a valuable topic in both academia and industry. Recently, large-scale mono-programming-lingual (MonoPL) pre-training models have been proposed to boost the performance of code completion. However, the code completion on low-resource programming languages (PL) is difficult for the data-driven paradigm, while there are plenty of developers using low-resource PLs. On the other hand, there are few studies exploring the effects of multi-programming-lingual (MultiPL) pre-training for the code completion, especially the impact on low-resource programming languages. To this end, we "},"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":"2212.09666","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-12-19T17:50:05Z","cross_cats_sorted":[],"title_canon_sha256":"a52c1b3c4acf4780ebc81b55afa2de4c69d6ac7a88589874dc8285e6836bcb3f","abstract_canon_sha256":"db43f54340d8ea2e090d6db32dc12357cc37cc67a3203fc13b96421ab17ac128"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:26:29.685389Z","signature_b64":"Cag8bETHD2KNoxWfeJCmID4vodD2LdsRqEC4drsGhu8YRgVCssjOpKfwwYLjx1FaoCtYPU2SMHGYYFmJo1RtCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8b020dde3c5b4921ce4e652df1df94069c3e0d65728060a9a3b88eadd51da8ca","last_reissued_at":"2026-07-05T05:26:29.684973Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:26:29.684973Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MultiCoder: Multi-Programming-Lingual Pre-Training for Low-Resource Code Completion","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Cuiyun Gao, Pingyi Zhou, Yasheng Wang, Yinpeng Guo, Zenglin Xu, Zi Gong","submitted_at":"2022-12-19T17:50:05Z","abstract_excerpt":"Code completion is a valuable topic in both academia and industry. Recently, large-scale mono-programming-lingual (MonoPL) pre-training models have been proposed to boost the performance of code completion. However, the code completion on low-resource programming languages (PL) is difficult for the data-driven paradigm, while there are plenty of developers using low-resource PLs. On the other hand, there are few studies exploring the effects of multi-programming-lingual (MultiPL) pre-training for the code completion, especially the impact on low-resource programming languages. To this end, we "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.09666","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/2212.09666/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":"2212.09666","created_at":"2026-07-05T05:26:29.685030+00:00"},{"alias_kind":"arxiv_version","alias_value":"2212.09666v1","created_at":"2026-07-05T05:26:29.685030+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.09666","created_at":"2026-07-05T05:26:29.685030+00:00"},{"alias_kind":"pith_short_12","alias_value":"RMBA3XR4LNES","created_at":"2026-07-05T05:26:29.685030+00:00"},{"alias_kind":"pith_short_16","alias_value":"RMBA3XR4LNESDTSO","created_at":"2026-07-05T05:26:29.685030+00:00"},{"alias_kind":"pith_short_8","alias_value":"RMBA3XR4","created_at":"2026-07-05T05:26:29.685030+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2504.15564","citing_title":"OpenClassGen: A Large-Scale Corpus of Real-World Python Classes for LLM Research","ref_index":17,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RMBA3XR4LNESDTSOMUW7DX4UA2","json":"https://pith.science/pith/RMBA3XR4LNESDTSOMUW7DX4UA2.json","graph_json":"https://pith.science/api/pith-number/RMBA3XR4LNESDTSOMUW7DX4UA2/graph.json","events_json":"https://pith.science/api/pith-number/RMBA3XR4LNESDTSOMUW7DX4UA2/events.json","paper":"https://pith.science/paper/RMBA3XR4"},"agent_actions":{"view_html":"https://pith.science/pith/RMBA3XR4LNESDTSOMUW7DX4UA2","download_json":"https://pith.science/pith/RMBA3XR4LNESDTSOMUW7DX4UA2.json","view_paper":"https://pith.science/paper/RMBA3XR4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2212.09666&json=true","fetch_graph":"https://pith.science/api/pith-number/RMBA3XR4LNESDTSOMUW7DX4UA2/graph.json","fetch_events":"https://pith.science/api/pith-number/RMBA3XR4LNESDTSOMUW7DX4UA2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RMBA3XR4LNESDTSOMUW7DX4UA2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RMBA3XR4LNESDTSOMUW7DX4UA2/action/storage_attestation","attest_author":"https://pith.science/pith/RMBA3XR4LNESDTSOMUW7DX4UA2/action/author_attestation","sign_citation":"https://pith.science/pith/RMBA3XR4LNESDTSOMUW7DX4UA2/action/citation_signature","submit_replication":"https://pith.science/pith/RMBA3XR4LNESDTSOMUW7DX4UA2/action/replication_record"}},"created_at":"2026-07-05T05:26:29.685030+00:00","updated_at":"2026-07-05T05:26:29.685030+00:00"}