{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:JVM7TSL3FD6T6IK5RUKMPGO6BK","short_pith_number":"pith:JVM7TSL3","canonical_record":{"source":{"id":"2202.09817","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-02-20T13:49:34Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"b97acb2026d1765a5d41975200b821ac45b7cb454282dad79bc88741331bf93a","abstract_canon_sha256":"fa4723695cd6315460f2b1364e69a4dd5f45cfa0ec3d4547733ee6d2a47616ab"},"schema_version":"1.0"},"canonical_sha256":"4d59f9c97b28fd3f215d8d14c799de0ab9f7a1c021399721746e22f2b86ef7b6","source":{"kind":"arxiv","id":"2202.09817","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2202.09817","created_at":"2026-07-05T05:31:12Z"},{"alias_kind":"arxiv_version","alias_value":"2202.09817v2","created_at":"2026-07-05T05:31:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.09817","created_at":"2026-07-05T05:31:12Z"},{"alias_kind":"pith_short_12","alias_value":"JVM7TSL3FD6T","created_at":"2026-07-05T05:31:12Z"},{"alias_kind":"pith_short_16","alias_value":"JVM7TSL3FD6T6IK5","created_at":"2026-07-05T05:31:12Z"},{"alias_kind":"pith_short_8","alias_value":"JVM7TSL3","created_at":"2026-07-05T05:31:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:JVM7TSL3FD6T6IK5RUKMPGO6BK","target":"record","payload":{"canonical_record":{"source":{"id":"2202.09817","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-02-20T13:49:34Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"b97acb2026d1765a5d41975200b821ac45b7cb454282dad79bc88741331bf93a","abstract_canon_sha256":"fa4723695cd6315460f2b1364e69a4dd5f45cfa0ec3d4547733ee6d2a47616ab"},"schema_version":"1.0"},"canonical_sha256":"4d59f9c97b28fd3f215d8d14c799de0ab9f7a1c021399721746e22f2b86ef7b6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:31:12.821292Z","signature_b64":"C4hDjToWuCRr8CHfwDzeniFBqQE+24tqatq9VCSSYiEGpn1d5/64vZ/0srlTylBsE5uJ5k3ALsIYe+prgGitAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4d59f9c97b28fd3f215d8d14c799de0ab9f7a1c021399721746e22f2b86ef7b6","last_reissued_at":"2026-07-05T05:31:12.820885Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:31:12.820885Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2202.09817","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:31:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uP/qAWtsd10A7VWE92aXO9Hm/uGXPvW0McWg5+5tYZt5cG7i4JEn3ph/FUutnkPNuC6RWvDIGlCoKzLkUIUaAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T03:46:47.337630Z"},"content_sha256":"c3a686890d24645c5bef648115eed8a2b6ed01cced27dfed0626422e6edd1009","schema_version":"1.0","event_id":"sha256:c3a686890d24645c5bef648115eed8a2b6ed01cced27dfed0626422e6edd1009"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:JVM7TSL3FD6T6IK5RUKMPGO6BK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"$\\mathcal{Y}$-Tuning: An Efficient Tuning Paradigm for Large-Scale Pre-Trained Models via Label Representation Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Chenxin An, Xipeng Qiu, Yitao Liu","submitted_at":"2022-02-20T13:49:34Z","abstract_excerpt":"With the success of large-scale pre-trained models (PTMs), how efficiently adapting PTMs to downstream tasks has attracted tremendous attention, especially for PTMs with billions of parameters. Although some parameter-efficient tuning paradigms have been proposed to address this problem, they still require large resources to compute the gradients in the training phase. In this paper, we propose $\\mathcal{Y}$-Tuning, an efficient yet effective paradigm to adapt frozen large-scale PTMs to specific downstream tasks. $\\mathcal{Y}$-tuning learns dense representations for labels $\\mathcal{Y}$ define"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.09817","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/2202.09817/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:31:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"d1RQzm/cIj8RngzJZbTSXTCN44aL7bcARyNKXPmHuHmpHFrvdNPQyvvoeuOld6KiwrnJgJslIcxgnOLv8iNoCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T03:46:47.338424Z"},"content_sha256":"fa50f9bf30f9a91df8a3f5646ac62deb422f29696d3a649e6ee35653f389a8b3","schema_version":"1.0","event_id":"sha256:fa50f9bf30f9a91df8a3f5646ac62deb422f29696d3a649e6ee35653f389a8b3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JVM7TSL3FD6T6IK5RUKMPGO6BK/bundle.json","state_url":"https://pith.science/pith/JVM7TSL3FD6T6IK5RUKMPGO6BK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JVM7TSL3FD6T6IK5RUKMPGO6BK/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-06T03:46:47Z","links":{"resolver":"https://pith.science/pith/JVM7TSL3FD6T6IK5RUKMPGO6BK","bundle":"https://pith.science/pith/JVM7TSL3FD6T6IK5RUKMPGO6BK/bundle.json","state":"https://pith.science/pith/JVM7TSL3FD6T6IK5RUKMPGO6BK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JVM7TSL3FD6T6IK5RUKMPGO6BK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:JVM7TSL3FD6T6IK5RUKMPGO6BK","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":"fa4723695cd6315460f2b1364e69a4dd5f45cfa0ec3d4547733ee6d2a47616ab","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-02-20T13:49:34Z","title_canon_sha256":"b97acb2026d1765a5d41975200b821ac45b7cb454282dad79bc88741331bf93a"},"schema_version":"1.0","source":{"id":"2202.09817","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2202.09817","created_at":"2026-07-05T05:31:12Z"},{"alias_kind":"arxiv_version","alias_value":"2202.09817v2","created_at":"2026-07-05T05:31:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.09817","created_at":"2026-07-05T05:31:12Z"},{"alias_kind":"pith_short_12","alias_value":"JVM7TSL3FD6T","created_at":"2026-07-05T05:31:12Z"},{"alias_kind":"pith_short_16","alias_value":"JVM7TSL3FD6T6IK5","created_at":"2026-07-05T05:31:12Z"},{"alias_kind":"pith_short_8","alias_value":"JVM7TSL3","created_at":"2026-07-05T05:31:12Z"}],"graph_snapshots":[{"event_id":"sha256:fa50f9bf30f9a91df8a3f5646ac62deb422f29696d3a649e6ee35653f389a8b3","target":"graph","created_at":"2026-07-05T05:31:12Z","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/2202.09817/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"With the success of large-scale pre-trained models (PTMs), how efficiently adapting PTMs to downstream tasks has attracted tremendous attention, especially for PTMs with billions of parameters. Although some parameter-efficient tuning paradigms have been proposed to address this problem, they still require large resources to compute the gradients in the training phase. In this paper, we propose $\\mathcal{Y}$-Tuning, an efficient yet effective paradigm to adapt frozen large-scale PTMs to specific downstream tasks. $\\mathcal{Y}$-tuning learns dense representations for labels $\\mathcal{Y}$ define","authors_text":"Chenxin An, Xipeng Qiu, Yitao Liu","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-02-20T13:49:34Z","title":"$\\mathcal{Y}$-Tuning: An Efficient Tuning Paradigm for Large-Scale Pre-Trained Models via Label Representation Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.09817","kind":"arxiv","version":2},"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:c3a686890d24645c5bef648115eed8a2b6ed01cced27dfed0626422e6edd1009","target":"record","created_at":"2026-07-05T05:31:12Z","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":"fa4723695cd6315460f2b1364e69a4dd5f45cfa0ec3d4547733ee6d2a47616ab","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-02-20T13:49:34Z","title_canon_sha256":"b97acb2026d1765a5d41975200b821ac45b7cb454282dad79bc88741331bf93a"},"schema_version":"1.0","source":{"id":"2202.09817","kind":"arxiv","version":2}},"canonical_sha256":"4d59f9c97b28fd3f215d8d14c799de0ab9f7a1c021399721746e22f2b86ef7b6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4d59f9c97b28fd3f215d8d14c799de0ab9f7a1c021399721746e22f2b86ef7b6","first_computed_at":"2026-07-05T05:31:12.820885Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:31:12.820885Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"C4hDjToWuCRr8CHfwDzeniFBqQE+24tqatq9VCSSYiEGpn1d5/64vZ/0srlTylBsE5uJ5k3ALsIYe+prgGitAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T05:31:12.821292Z","signed_message":"canonical_sha256_bytes"},"source_id":"2202.09817","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c3a686890d24645c5bef648115eed8a2b6ed01cced27dfed0626422e6edd1009","sha256:fa50f9bf30f9a91df8a3f5646ac62deb422f29696d3a649e6ee35653f389a8b3"],"state_sha256":"f4def7bf95362028d04248b87cd664b6153158e24c796dd5f511f58b93710ae0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yN1srXWSvPKkv60PIDLgrUhZNU/upWnaiHhbYxY9BowVAG904yrC1Pqzpz1TOfsDlFD37lZVz0NCIAj/c714Cg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T03:46:47.345485Z","bundle_sha256":"474f3779a2f554caa18a20799bdf05d38771952a421933fb0825887a4297bd2d"}}