{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:XDK6SHP7SWLSWNAPR525LPOTG7","short_pith_number":"pith:XDK6SHP7","canonical_record":{"source":{"id":"2202.12312","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-02-24T19:00:39Z","cross_cats_sorted":[],"title_canon_sha256":"f8c359f38671fd4b68b1448c374aa0f0a73d717f7e6090570f39c1f695fffef2","abstract_canon_sha256":"e8778266d82b273618dba6fc7f0b6fd50c0586ff66125052488359ae578d0d2a"},"schema_version":"1.0"},"canonical_sha256":"b8d5e91dff95972b340f8f75d5bdd337c09a3afb2859a6b3688ad8554495bc7c","source":{"kind":"arxiv","id":"2202.12312","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2202.12312","created_at":"2026-07-05T07:36:48Z"},{"alias_kind":"arxiv_version","alias_value":"2202.12312v2","created_at":"2026-07-05T07:36:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.12312","created_at":"2026-07-05T07:36:48Z"},{"alias_kind":"pith_short_12","alias_value":"XDK6SHP7SWLS","created_at":"2026-07-05T07:36:48Z"},{"alias_kind":"pith_short_16","alias_value":"XDK6SHP7SWLSWNAP","created_at":"2026-07-05T07:36:48Z"},{"alias_kind":"pith_short_8","alias_value":"XDK6SHP7","created_at":"2026-07-05T07:36:48Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:XDK6SHP7SWLSWNAPR525LPOTG7","target":"record","payload":{"canonical_record":{"source":{"id":"2202.12312","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-02-24T19:00:39Z","cross_cats_sorted":[],"title_canon_sha256":"f8c359f38671fd4b68b1448c374aa0f0a73d717f7e6090570f39c1f695fffef2","abstract_canon_sha256":"e8778266d82b273618dba6fc7f0b6fd50c0586ff66125052488359ae578d0d2a"},"schema_version":"1.0"},"canonical_sha256":"b8d5e91dff95972b340f8f75d5bdd337c09a3afb2859a6b3688ad8554495bc7c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:36:48.823876Z","signature_b64":"12azPa1WPm8Kw4VOL6Te4ES4NTDKRIm5cCSaOto4iXutC2iiL/L0v5X8wLgMCRU/eT9ItrRiqUFUOw9FzWE0CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b8d5e91dff95972b340f8f75d5bdd337c09a3afb2859a6b3688ad8554495bc7c","last_reissued_at":"2026-07-05T07:36:48.823420Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:36:48.823420Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2202.12312","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":"2026-07-05T07:36:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Y8DRKdtUxQfyWYwbUgrW38bSccXWuRpgLldgzUuI8qsPZxXDUSPGDwARxQ3y2Dz4guT1w2qwclleCaWIcFl6AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T21:15:11.405172Z"},"content_sha256":"d582431d66f1d9ad99377f878edfb95b1f579d73746ab6f13cf4c484fe5ef95f","schema_version":"1.0","event_id":"sha256:d582431d66f1d9ad99377f878edfb95b1f579d73746ab6f13cf4c484fe5ef95f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:XDK6SHP7SWLSWNAPR525LPOTG7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Oolong: Investigating What Makes Transfer Learning Hard with Controlled Studies","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Alex Tamkin, Isabel Papadimitriou, Zhengxuan Wu","submitted_at":"2022-02-24T19:00:39Z","abstract_excerpt":"When we transfer a pretrained language model to a new language, there are many axes of variation that change at once. To disentangle the impact of different factors like syntactic similarity and vocabulary similarity, we propose a set of controlled transfer studies: we systematically transform the language of the GLUE benchmark, altering one axis of crosslingual variation at a time, and then measure the resulting drops in a pretrained model's downstream performance. We find that models can largely recover from syntactic-style shifts, but cannot recover from vocabulary misalignment and embeddin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.12312","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/2202.12312/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":"2026-07-05T07:36:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tV4dNUfPT4bMofu3PX5bYsFEeECXpAZ1WUiX3VLuoJl9ni/ZBVL/QLvg29eAuWX5PMb7cJ9Rex26BCW1ASCWCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T21:15:11.405557Z"},"content_sha256":"fd0715c658e446bda6b78509fd33360194127fd798d519c1899ffdce87a430e9","schema_version":"1.0","event_id":"sha256:fd0715c658e446bda6b78509fd33360194127fd798d519c1899ffdce87a430e9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XDK6SHP7SWLSWNAPR525LPOTG7/bundle.json","state_url":"https://pith.science/pith/XDK6SHP7SWLSWNAPR525LPOTG7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XDK6SHP7SWLSWNAPR525LPOTG7/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":"2026-07-09T21:15:11Z","links":{"resolver":"https://pith.science/pith/XDK6SHP7SWLSWNAPR525LPOTG7","bundle":"https://pith.science/pith/XDK6SHP7SWLSWNAPR525LPOTG7/bundle.json","state":"https://pith.science/pith/XDK6SHP7SWLSWNAPR525LPOTG7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XDK6SHP7SWLSWNAPR525LPOTG7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:XDK6SHP7SWLSWNAPR525LPOTG7","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":"e8778266d82b273618dba6fc7f0b6fd50c0586ff66125052488359ae578d0d2a","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-02-24T19:00:39Z","title_canon_sha256":"f8c359f38671fd4b68b1448c374aa0f0a73d717f7e6090570f39c1f695fffef2"},"schema_version":"1.0","source":{"id":"2202.12312","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2202.12312","created_at":"2026-07-05T07:36:48Z"},{"alias_kind":"arxiv_version","alias_value":"2202.12312v2","created_at":"2026-07-05T07:36:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.12312","created_at":"2026-07-05T07:36:48Z"},{"alias_kind":"pith_short_12","alias_value":"XDK6SHP7SWLS","created_at":"2026-07-05T07:36:48Z"},{"alias_kind":"pith_short_16","alias_value":"XDK6SHP7SWLSWNAP","created_at":"2026-07-05T07:36:48Z"},{"alias_kind":"pith_short_8","alias_value":"XDK6SHP7","created_at":"2026-07-05T07:36:48Z"}],"graph_snapshots":[{"event_id":"sha256:fd0715c658e446bda6b78509fd33360194127fd798d519c1899ffdce87a430e9","target":"graph","created_at":"2026-07-05T07:36:48Z","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/2202.12312/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"When we transfer a pretrained language model to a new language, there are many axes of variation that change at once. To disentangle the impact of different factors like syntactic similarity and vocabulary similarity, we propose a set of controlled transfer studies: we systematically transform the language of the GLUE benchmark, altering one axis of crosslingual variation at a time, and then measure the resulting drops in a pretrained model's downstream performance. We find that models can largely recover from syntactic-style shifts, but cannot recover from vocabulary misalignment and embeddin","authors_text":"Alex Tamkin, Isabel Papadimitriou, Zhengxuan Wu","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-02-24T19:00:39Z","title":"Oolong: Investigating What Makes Transfer Learning Hard with Controlled Studies"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.12312","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:d582431d66f1d9ad99377f878edfb95b1f579d73746ab6f13cf4c484fe5ef95f","target":"record","created_at":"2026-07-05T07:36:48Z","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":"e8778266d82b273618dba6fc7f0b6fd50c0586ff66125052488359ae578d0d2a","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-02-24T19:00:39Z","title_canon_sha256":"f8c359f38671fd4b68b1448c374aa0f0a73d717f7e6090570f39c1f695fffef2"},"schema_version":"1.0","source":{"id":"2202.12312","kind":"arxiv","version":2}},"canonical_sha256":"b8d5e91dff95972b340f8f75d5bdd337c09a3afb2859a6b3688ad8554495bc7c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b8d5e91dff95972b340f8f75d5bdd337c09a3afb2859a6b3688ad8554495bc7c","first_computed_at":"2026-07-05T07:36:48.823420Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:36:48.823420Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"12azPa1WPm8Kw4VOL6Te4ES4NTDKRIm5cCSaOto4iXutC2iiL/L0v5X8wLgMCRU/eT9ItrRiqUFUOw9FzWE0CQ==","signature_status":"signed_v1","signed_at":"2026-07-05T07:36:48.823876Z","signed_message":"canonical_sha256_bytes"},"source_id":"2202.12312","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d582431d66f1d9ad99377f878edfb95b1f579d73746ab6f13cf4c484fe5ef95f","sha256:fd0715c658e446bda6b78509fd33360194127fd798d519c1899ffdce87a430e9"],"state_sha256":"7e4a765d79f936002e5705cc30968ab07de0deb5f9a45c00596adcfd04f9d061"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"S4J2S23d889B90MFpd1hehqWucKFtBUPGapxa1yMmdynh0p3O59BTeTf55qisZrupyN+6iCckseCnyyZ/GkMBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-09T21:15:11.407618Z","bundle_sha256":"3f48910671390a8c4ee9f6b23a2e2c55acd8639fe3d2946e9da78b94f2e934e4"}}