{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:YBWVYCL5SM74F2MACKP6TRTWX3","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":"4c157836dd822aa9813277c50c0207116acd4c448712645015dba6fc12eea704","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-05-24T14:51:01Z","title_canon_sha256":"ef6144d704c0e5ac1152238600ef2b5497a3c68d54460b51adf078a8b00ac4e0"},"schema_version":"1.0","source":{"id":"2305.15212","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.15212","created_at":"2026-07-05T06:13:32Z"},{"alias_kind":"arxiv_version","alias_value":"2305.15212v1","created_at":"2026-07-05T06:13:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.15212","created_at":"2026-07-05T06:13:32Z"},{"alias_kind":"pith_short_12","alias_value":"YBWVYCL5SM74","created_at":"2026-07-05T06:13:32Z"},{"alias_kind":"pith_short_16","alias_value":"YBWVYCL5SM74F2MA","created_at":"2026-07-05T06:13:32Z"},{"alias_kind":"pith_short_8","alias_value":"YBWVYCL5","created_at":"2026-07-05T06:13:32Z"}],"graph_snapshots":[{"event_id":"sha256:5f29377eba477ac254e2dee0b8e250b319a6cbd270bcd310e63130d66fb658d4","target":"graph","created_at":"2026-07-05T06:13:32Z","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/2305.15212/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Fine-tuning large pre-trained language models on various downstream tasks with whole parameters is prohibitively expensive. Hence, Parameter-efficient fine-tuning has attracted attention that only optimizes a few task-specific parameters with the frozen pre-trained model. In this work, we focus on prefix tuning, which only optimizes continuous prefix vectors (i.e. pseudo tokens) inserted into Transformer layers. Based on the observation that the learned syntax and semantics representation varies a lot at different layers, we argue that the adaptive prefix will be further tailored to each layer","authors_text":"Chengyu Wang, Chuanqi Tan, Haiyang Xu, Jun Huang, Songfang Huang, Zhen-Ru Zhang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-05-24T14:51:01Z","title":"Towards Adaptive Prefix Tuning for Parameter-Efficient Language Model Fine-tuning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.15212","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:d0572f234187a87cd6fa489c48cf881e991f07d52a11b4e84f0146ce8e1b38e5","target":"record","created_at":"2026-07-05T06:13:32Z","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":"4c157836dd822aa9813277c50c0207116acd4c448712645015dba6fc12eea704","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-05-24T14:51:01Z","title_canon_sha256":"ef6144d704c0e5ac1152238600ef2b5497a3c68d54460b51adf078a8b00ac4e0"},"schema_version":"1.0","source":{"id":"2305.15212","kind":"arxiv","version":1}},"canonical_sha256":"c06d5c097d933fc2e980129fe9c676bed2ebc4aa46256d02851b5e432a9484e8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c06d5c097d933fc2e980129fe9c676bed2ebc4aa46256d02851b5e432a9484e8","first_computed_at":"2026-07-05T06:13:32.564671Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:13:32.564671Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"WlP7ILDB7ldbNx8y0eRMYmisz+BBNCOkKcT22fYQhTZWwO89Nj4zDAbZiqaz7gn0AONtOdTNuvZipxZVsrVpDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T06:13:32.565198Z","signed_message":"canonical_sha256_bytes"},"source_id":"2305.15212","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d0572f234187a87cd6fa489c48cf881e991f07d52a11b4e84f0146ce8e1b38e5","sha256:5f29377eba477ac254e2dee0b8e250b319a6cbd270bcd310e63130d66fb658d4"],"state_sha256":"3ca8f9f015d40693a92b66cdb06fe983663fc4d421600d1910401aaefb2ab97c"}