{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:22MOZ5NHGMI3GMV4PKIDBGLZUR","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":"ce22ded20f9e0668daebf8aba8eda9096f346b1e7e4cabef65ddfe2b2c1203ad","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-04-21T11:02:09Z","title_canon_sha256":"27f74978d9fb8d8f87237cbc8a6df7b1bed80f03e86612a960ae14c59d559b87"},"schema_version":"1.0","source":{"id":"2204.10019","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2204.10019","created_at":"2026-07-05T04:16:46Z"},{"alias_kind":"arxiv_version","alias_value":"2204.10019v1","created_at":"2026-07-05T04:16:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2204.10019","created_at":"2026-07-05T04:16:46Z"},{"alias_kind":"pith_short_12","alias_value":"22MOZ5NHGMI3","created_at":"2026-07-05T04:16:46Z"},{"alias_kind":"pith_short_16","alias_value":"22MOZ5NHGMI3GMV4","created_at":"2026-07-05T04:16:46Z"},{"alias_kind":"pith_short_8","alias_value":"22MOZ5NH","created_at":"2026-07-05T04:16:46Z"}],"graph_snapshots":[{"event_id":"sha256:fdb968a79480ac8d510ada3dffa3b9c6a64f52edb3433b404ac048e763155d82","target":"graph","created_at":"2026-07-05T04:16:46Z","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/2204.10019/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Huge pretrained language models (LMs) have demonstrated surprisingly good zero-shot capabilities on a wide variety of tasks. This gives rise to the appealing vision of a single, versatile model with a wide range of functionalities across disparate applications. However, current leading techniques for leveraging a \"frozen\" LM -- i.e., leaving its weights untouched -- still often underperform fine-tuning approaches which modify these weights in a task-dependent way. Those, in turn, suffer forgetfulness and compromise versatility, suggesting a tradeoff between performance and versatility. The mai","authors_text":"Amnon Shashua, Barak Lenz, Daniel Jannai, Dor Muhlgay, Itay Dalmedigos, Kevin Leyton-Brown, Opher Lieber, Ori Ram, Shai Shalev-Shwartz, Yoav Levine, Yoav Shoham, Yoel Zeldes, Yoni Osin","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-04-21T11:02:09Z","title":"Standing on the Shoulders of Giant Frozen Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2204.10019","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:cbb76c12ff8c0fa57217fdba2d374adc10609855951b8d30491b0d121c780401","target":"record","created_at":"2026-07-05T04:16:46Z","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":"ce22ded20f9e0668daebf8aba8eda9096f346b1e7e4cabef65ddfe2b2c1203ad","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-04-21T11:02:09Z","title_canon_sha256":"27f74978d9fb8d8f87237cbc8a6df7b1bed80f03e86612a960ae14c59d559b87"},"schema_version":"1.0","source":{"id":"2204.10019","kind":"arxiv","version":1}},"canonical_sha256":"d698ecf5a73311b332bc7a90309979a473df208dd9908f8e9d2ff5b780b7b116","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d698ecf5a73311b332bc7a90309979a473df208dd9908f8e9d2ff5b780b7b116","first_computed_at":"2026-07-05T04:16:46.112436Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:16:46.112436Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"of167wz4igSS17ydnY0jTx6nonvDyRA/IvuAx/BPpEV05yIk0qeIcl1C5QAtn2JEabA1cnbu0EwewURl2EGiCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T04:16:46.112829Z","signed_message":"canonical_sha256_bytes"},"source_id":"2204.10019","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:cbb76c12ff8c0fa57217fdba2d374adc10609855951b8d30491b0d121c780401","sha256:fdb968a79480ac8d510ada3dffa3b9c6a64f52edb3433b404ac048e763155d82"],"state_sha256":"da343fe784ec9e02267cc36eb38308c21a09257b96d9957e8217cdd29654a3af"}