{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:SYODJXACIFMIBZ67H3A5K2W5B7","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":"f19a91522dbfee65eb4a2672602eac9997d1b02c06d71c06b709df82273ed0fb","cross_cats_sorted":["cs.CL","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SE","submitted_at":"2023-08-21T04:31:06Z","title_canon_sha256":"29625c1f3c5566900a5bcd8cf7e4290335fc41e0912886748103e57a4f8380d5"},"schema_version":"1.0","source":{"id":"2308.10462","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2308.10462","created_at":"2026-07-05T09:54:23Z"},{"alias_kind":"arxiv_version","alias_value":"2308.10462v3","created_at":"2026-07-05T09:54:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.10462","created_at":"2026-07-05T09:54:23Z"},{"alias_kind":"pith_short_12","alias_value":"SYODJXACIFMI","created_at":"2026-07-05T09:54:23Z"},{"alias_kind":"pith_short_16","alias_value":"SYODJXACIFMIBZ67","created_at":"2026-07-05T09:54:23Z"},{"alias_kind":"pith_short_8","alias_value":"SYODJXAC","created_at":"2026-07-05T09:54:23Z"}],"graph_snapshots":[{"event_id":"sha256:2ed853d84d8c712970f6f2e592a8665805bf05b6fb1f3e894c410453859a19ce","target":"graph","created_at":"2026-07-05T09:54:23Z","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/2308.10462/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large language models (LLMs) demonstrate impressive capabilities to generate accurate code snippets given natural language intents in a zero-shot manner, i.e., without the need for specific fine-tuning. While prior studies have highlighted the advantages of fine-tuning LLMs, this process incurs high computational costs, making it impractical in resource-scarce environments, particularly for models with billions of parameters. To address these challenges, previous research explored in-context learning (ICL) and retrieval-augmented generation (RAG) as strategies to guide the LLM generative proce","authors_text":"David Lo, Houari Sahraoui, Kisub Kim, Martin Weyssow, Xin Zhou","cross_cats":["cs.CL","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SE","submitted_at":"2023-08-21T04:31:06Z","title":"Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.10462","kind":"arxiv","version":3},"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":"sha256:98622ee1ff1aaedc6489858d5dae02cdd7887adee4f0af279380b32abd294269","target":"record","created_at":"2026-07-05T09:54:23Z","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":"f19a91522dbfee65eb4a2672602eac9997d1b02c06d71c06b709df82273ed0fb","cross_cats_sorted":["cs.CL","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SE","submitted_at":"2023-08-21T04:31:06Z","title_canon_sha256":"29625c1f3c5566900a5bcd8cf7e4290335fc41e0912886748103e57a4f8380d5"},"schema_version":"1.0","source":{"id":"2308.10462","kind":"arxiv","version":3}},"canonical_sha256":"961c34dc02415880e7df3ec1d56add0fe023b03ea6f96b2d8db40a3139c250e7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"961c34dc02415880e7df3ec1d56add0fe023b03ea6f96b2d8db40a3139c250e7","first_computed_at":"2026-07-05T09:54:23.541723Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:54:23.541723Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Xk/WhB8Hi8BhDuHSD+uzpd0LO6NczQVbuWx9w2jMMWA1H+U6XBJaw0wdLWp32dUcF7yZRswif/aI2uUyFGuWCg==","signature_status":"signed_v1","signed_at":"2026-07-05T09:54:23.542184Z","signed_message":"canonical_sha256_bytes"},"source_id":"2308.10462","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:98622ee1ff1aaedc6489858d5dae02cdd7887adee4f0af279380b32abd294269","sha256:2ed853d84d8c712970f6f2e592a8665805bf05b6fb1f3e894c410453859a19ce"],"state_sha256":"7c7625fcb9df2f941f4d3ff6033ae7a31e594ed61229ad1debaf52a0b9332470"}