{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:6JO3GVYMNIBJNAHXQCZOAOFZSJ","short_pith_number":"pith:6JO3GVYM","schema_version":"1.0","canonical_sha256":"f25db3570c6a029680f780b2e038b9924334880ea696f1c10fb39a2c4c2c6d27","source":{"kind":"arxiv","id":"2206.01335","version":2},"attestation_state":"computed","paper":{"title":"Code Generation Tools (Almost) for Free? A Study of Few-Shot, Pre-Trained Language Models on Code","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.SE","authors_text":"Beatriz Souza, Marcelo d'Amorim, Michael Pradel, Patrick Barei{\\ss}","submitted_at":"2022-06-02T23:15:42Z","abstract_excerpt":"Few-shot learning with large-scale, pre-trained language models is a powerful way to answer questions about code, e.g., how to complete a given code example, or even generate code snippets from scratch. The success of these models raises the question whether they could serve as a basis for building a wide range code generation tools. Traditionally, such tools are built manually and separately for each task. Instead, few-shot learning may allow to obtain different tools from a single pre-trained language model by simply providing a few examples or a natural language description of the expected "},"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":"2206.01335","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SE","submitted_at":"2022-06-02T23:15:42Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"0f3ed229ff3f9bb8932d7e2167068a4f744f2625a997109c255f8bba005d79ed","abstract_canon_sha256":"2e225122a2ea2755aab4771ab3e25be17aa5d5a182b82adf3e390f5cad2375f3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:30:57.022997Z","signature_b64":"WApJmHrUZmmEJo+5DZdK+OgxyUPYrQ/80iyiKxfmlxUVgp5RUAHWuy5Kw0bZO/YzV4cvDfyAxZqmoTGNQjuQCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f25db3570c6a029680f780b2e038b9924334880ea696f1c10fb39a2c4c2c6d27","last_reissued_at":"2026-07-05T04:30:57.022450Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:30:57.022450Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Code Generation Tools (Almost) for Free? A Study of Few-Shot, Pre-Trained Language Models on Code","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.SE","authors_text":"Beatriz Souza, Marcelo d'Amorim, Michael Pradel, Patrick Barei{\\ss}","submitted_at":"2022-06-02T23:15:42Z","abstract_excerpt":"Few-shot learning with large-scale, pre-trained language models is a powerful way to answer questions about code, e.g., how to complete a given code example, or even generate code snippets from scratch. The success of these models raises the question whether they could serve as a basis for building a wide range code generation tools. Traditionally, such tools are built manually and separately for each task. Instead, few-shot learning may allow to obtain different tools from a single pre-trained language model by simply providing a few examples or a natural language description of the expected "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.01335","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/2206.01335/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":"2206.01335","created_at":"2026-07-05T04:30:57.022505+00:00"},{"alias_kind":"arxiv_version","alias_value":"2206.01335v2","created_at":"2026-07-05T04:30:57.022505+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.01335","created_at":"2026-07-05T04:30:57.022505+00:00"},{"alias_kind":"pith_short_12","alias_value":"6JO3GVYMNIBJ","created_at":"2026-07-05T04:30:57.022505+00:00"},{"alias_kind":"pith_short_16","alias_value":"6JO3GVYMNIBJNAHX","created_at":"2026-07-05T04:30:57.022505+00:00"},{"alias_kind":"pith_short_8","alias_value":"6JO3GVYM","created_at":"2026-07-05T04:30:57.022505+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.07029","citing_title":"Gimitest: A Comprehensive Tool for Testing Reinforcement Learning Policies","ref_index":1,"is_internal_anchor":true},{"citing_arxiv_id":"2606.00530","citing_title":"Sakura: An Approach for Generating Complex Tests from Natural Language Test Descriptions","ref_index":15,"is_internal_anchor":false},{"citing_arxiv_id":"2509.11787","citing_title":"CodeCureAgent: Automatic Classification and Repair of Static Analysis Warnings","ref_index":4,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6JO3GVYMNIBJNAHXQCZOAOFZSJ","json":"https://pith.science/pith/6JO3GVYMNIBJNAHXQCZOAOFZSJ.json","graph_json":"https://pith.science/api/pith-number/6JO3GVYMNIBJNAHXQCZOAOFZSJ/graph.json","events_json":"https://pith.science/api/pith-number/6JO3GVYMNIBJNAHXQCZOAOFZSJ/events.json","paper":"https://pith.science/paper/6JO3GVYM"},"agent_actions":{"view_html":"https://pith.science/pith/6JO3GVYMNIBJNAHXQCZOAOFZSJ","download_json":"https://pith.science/pith/6JO3GVYMNIBJNAHXQCZOAOFZSJ.json","view_paper":"https://pith.science/paper/6JO3GVYM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2206.01335&json=true","fetch_graph":"https://pith.science/api/pith-number/6JO3GVYMNIBJNAHXQCZOAOFZSJ/graph.json","fetch_events":"https://pith.science/api/pith-number/6JO3GVYMNIBJNAHXQCZOAOFZSJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6JO3GVYMNIBJNAHXQCZOAOFZSJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6JO3GVYMNIBJNAHXQCZOAOFZSJ/action/storage_attestation","attest_author":"https://pith.science/pith/6JO3GVYMNIBJNAHXQCZOAOFZSJ/action/author_attestation","sign_citation":"https://pith.science/pith/6JO3GVYMNIBJNAHXQCZOAOFZSJ/action/citation_signature","submit_replication":"https://pith.science/pith/6JO3GVYMNIBJNAHXQCZOAOFZSJ/action/replication_record"}},"created_at":"2026-07-05T04:30:57.022505+00:00","updated_at":"2026-07-05T04:30:57.022505+00:00"}