{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:DSWBWUNPLXWRZXRUROOALM5UUZ","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":"41515bbd24641e9eba58d10609769acf07fc328a2199d209eb8f2e91cafbf820","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-05-06T05:35:14Z","title_canon_sha256":"4956a5327695c8a6184cf76fca3cba9a7aa62551e72380433f42cadb9cc33acb"},"schema_version":"1.0","source":{"id":"2305.03937","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.03937","created_at":"2026-07-05T06:07:42Z"},{"alias_kind":"arxiv_version","alias_value":"2305.03937v1","created_at":"2026-07-05T06:07:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.03937","created_at":"2026-07-05T06:07:42Z"},{"alias_kind":"pith_short_12","alias_value":"DSWBWUNPLXWR","created_at":"2026-07-05T06:07:42Z"},{"alias_kind":"pith_short_16","alias_value":"DSWBWUNPLXWRZXRU","created_at":"2026-07-05T06:07:42Z"},{"alias_kind":"pith_short_8","alias_value":"DSWBWUNP","created_at":"2026-07-05T06:07:42Z"}],"graph_snapshots":[{"event_id":"sha256:12c7dd8bce5faa883d92d5722ab0d8b6fabdc00b385d18e4e1ec95563e24429f","target":"graph","created_at":"2026-07-05T06:07:42Z","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/2305.03937/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Prompt tuning is one of the successful approaches for parameter-efficient tuning of pre-trained language models. Despite being arguably the most parameter-efficient (tuned soft prompts constitute <0.1% of total parameters), it typically performs worse than other efficient tuning methods and is quite sensitive to hyper-parameters. In this work, we introduce Residual Prompt Tuning - a simple and efficient method that significantly improves the performance and stability of prompt tuning. We propose to reparameterize soft prompt embeddings using a shallow network with a residual connection. Our ex","authors_text":"Amjad Almahairi, Anastasia Razdaibiedina, Jimmy Ba, Madian Khabsa, Mike Lewis, Rui Hou, Yuning Mao","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-05-06T05:35:14Z","title":"Residual Prompt Tuning: Improving Prompt Tuning with Residual Reparameterization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.03937","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:f6872bfef0395f24bbbab7378d64d4f4482893df7d0a35a7f21b6c0328a90ce9","target":"record","created_at":"2026-07-05T06:07:42Z","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":"41515bbd24641e9eba58d10609769acf07fc328a2199d209eb8f2e91cafbf820","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-05-06T05:35:14Z","title_canon_sha256":"4956a5327695c8a6184cf76fca3cba9a7aa62551e72380433f42cadb9cc33acb"},"schema_version":"1.0","source":{"id":"2305.03937","kind":"arxiv","version":1}},"canonical_sha256":"1cac1b51af5ded1cde348b9c05b3b4a6788701a15957ec743aff4a1058340e5a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1cac1b51af5ded1cde348b9c05b3b4a6788701a15957ec743aff4a1058340e5a","first_computed_at":"2026-07-05T06:07:42.861424Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:07:42.861424Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"v6GDR9oBD4X2BHN4eCLa1ChCQU+e+Y870EKwVWuMzcMlBM2B/H76Sl0UoVPILK95fNIdyH8Ir1FJKRI3F1oDCg==","signature_status":"signed_v1","signed_at":"2026-07-05T06:07:42.861819Z","signed_message":"canonical_sha256_bytes"},"source_id":"2305.03937","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f6872bfef0395f24bbbab7378d64d4f4482893df7d0a35a7f21b6c0328a90ce9","sha256:12c7dd8bce5faa883d92d5722ab0d8b6fabdc00b385d18e4e1ec95563e24429f"],"state_sha256":"1a95cb7a852758d5db612635f266c70298afca5333df93c6f78d23b3245797d4"}