{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:ONORYXJYYUUCI5OB2IG3M4WFH2","short_pith_number":"pith:ONORYXJY","canonical_record":{"source":{"id":"2205.02979","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-05-06T01:51:19Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"096cf68c08f5bc9be56cc12ad910e4de0069f2be54871638334fafd0f697d1cb","abstract_canon_sha256":"286816e5914bb8931e785846da69a9f2e7cb513565735da2819988e17a885299"},"schema_version":"1.0"},"canonical_sha256":"735d1c5d38c5282475c1d20db672c53e8e41628dae7c2d58ba84df24b668df90","source":{"kind":"arxiv","id":"2205.02979","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2205.02979","created_at":"2026-07-05T04:20:47Z"},{"alias_kind":"arxiv_version","alias_value":"2205.02979v1","created_at":"2026-07-05T04:20:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.02979","created_at":"2026-07-05T04:20:47Z"},{"alias_kind":"pith_short_12","alias_value":"ONORYXJYYUUC","created_at":"2026-07-05T04:20:47Z"},{"alias_kind":"pith_short_16","alias_value":"ONORYXJYYUUCI5OB","created_at":"2026-07-05T04:20:47Z"},{"alias_kind":"pith_short_8","alias_value":"ONORYXJY","created_at":"2026-07-05T04:20:47Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:ONORYXJYYUUCI5OB2IG3M4WFH2","target":"record","payload":{"canonical_record":{"source":{"id":"2205.02979","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-05-06T01:51:19Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"096cf68c08f5bc9be56cc12ad910e4de0069f2be54871638334fafd0f697d1cb","abstract_canon_sha256":"286816e5914bb8931e785846da69a9f2e7cb513565735da2819988e17a885299"},"schema_version":"1.0"},"canonical_sha256":"735d1c5d38c5282475c1d20db672c53e8e41628dae7c2d58ba84df24b668df90","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:20:47.070374Z","signature_b64":"NT0PoQ8PCoUhaW71MXg8pXWh6rSOkHgjxvxV0IYLSVghRwC+q5ES4xXc/1icB6jZAcMb3bWIwrgxD6wXHwf+Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"735d1c5d38c5282475c1d20db672c53e8e41628dae7c2d58ba84df24b668df90","last_reissued_at":"2026-07-05T04:20:47.069888Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:20:47.069888Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2205.02979","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:20:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Hck0hB1FjWVjfSFhTKGeaISxhTsDyEt6DpN0AxJ9i8YfrATZktc8U9XOaxtDodsCJtwgZE449fV8MiDSfqTtBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-17T07:58:24.215280Z"},"content_sha256":"d6f184ad82f26510e05810edf242384ed131c97f1c43f09e5a3ac43273e189e2","schema_version":"1.0","event_id":"sha256:d6f184ad82f26510e05810edf242384ed131c97f1c43f09e5a3ac43273e189e2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:ONORYXJYYUUCI5OB2IG3M4WFH2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Explaining the Effectiveness of Multi-Task Learning for Efficient Knowledge Extraction from Spine MRI Reports","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.LG","authors_text":"Anasuya Das, Arijit Sehanobish, Benjamin Odry, Danielle Torres, Jayashri Pawar, McCullen Sandora, Murray Becker, Nabila Abraham, Richard Herzog, Ron Vianu","submitted_at":"2022-05-06T01:51:19Z","abstract_excerpt":"Pretrained Transformer based models finetuned on domain specific corpora have changed the landscape of NLP. However, training or fine-tuning these models for individual tasks can be time consuming and resource intensive. Thus, a lot of current research is focused on using transformers for multi-task learning (Raffel et al.,2020) and how to group the tasks to help a multi-task model to learn effective representations that can be shared across tasks (Standley et al., 2020; Fifty et al., 2021). In this work, we show that a single multi-tasking model can match the performance of task specific mode"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.02979","kind":"arxiv","version":1},"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/2205.02979/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":"2026-07-05T04:20:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ErPbVckSNkB6+E122SfBuGWJ5s4ombMQ3ORkiHYEQxL8lqhdzrQ9R612jZ7JfiSqlGYzbRC+vCdV2xJigK9PCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-17T07:58:24.216010Z"},"content_sha256":"92e10279ac644f7b23d7abe8c64c375a66e7fdfdc51fdee2f7313a9ed47889d9","schema_version":"1.0","event_id":"sha256:92e10279ac644f7b23d7abe8c64c375a66e7fdfdc51fdee2f7313a9ed47889d9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ONORYXJYYUUCI5OB2IG3M4WFH2/bundle.json","state_url":"https://pith.science/pith/ONORYXJYYUUCI5OB2IG3M4WFH2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ONORYXJYYUUCI5OB2IG3M4WFH2/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":"2026-07-17T07:58:24Z","links":{"resolver":"https://pith.science/pith/ONORYXJYYUUCI5OB2IG3M4WFH2","bundle":"https://pith.science/pith/ONORYXJYYUUCI5OB2IG3M4WFH2/bundle.json","state":"https://pith.science/pith/ONORYXJYYUUCI5OB2IG3M4WFH2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ONORYXJYYUUCI5OB2IG3M4WFH2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:ONORYXJYYUUCI5OB2IG3M4WFH2","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":"286816e5914bb8931e785846da69a9f2e7cb513565735da2819988e17a885299","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-05-06T01:51:19Z","title_canon_sha256":"096cf68c08f5bc9be56cc12ad910e4de0069f2be54871638334fafd0f697d1cb"},"schema_version":"1.0","source":{"id":"2205.02979","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2205.02979","created_at":"2026-07-05T04:20:47Z"},{"alias_kind":"arxiv_version","alias_value":"2205.02979v1","created_at":"2026-07-05T04:20:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.02979","created_at":"2026-07-05T04:20:47Z"},{"alias_kind":"pith_short_12","alias_value":"ONORYXJYYUUC","created_at":"2026-07-05T04:20:47Z"},{"alias_kind":"pith_short_16","alias_value":"ONORYXJYYUUCI5OB","created_at":"2026-07-05T04:20:47Z"},{"alias_kind":"pith_short_8","alias_value":"ONORYXJY","created_at":"2026-07-05T04:20:47Z"}],"graph_snapshots":[{"event_id":"sha256:92e10279ac644f7b23d7abe8c64c375a66e7fdfdc51fdee2f7313a9ed47889d9","target":"graph","created_at":"2026-07-05T04:20:47Z","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/2205.02979/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Pretrained Transformer based models finetuned on domain specific corpora have changed the landscape of NLP. However, training or fine-tuning these models for individual tasks can be time consuming and resource intensive. Thus, a lot of current research is focused on using transformers for multi-task learning (Raffel et al.,2020) and how to group the tasks to help a multi-task model to learn effective representations that can be shared across tasks (Standley et al., 2020; Fifty et al., 2021). In this work, we show that a single multi-tasking model can match the performance of task specific mode","authors_text":"Anasuya Das, Arijit Sehanobish, Benjamin Odry, Danielle Torres, Jayashri Pawar, McCullen Sandora, Murray Becker, Nabila Abraham, Richard Herzog, Ron Vianu","cross_cats":["cs.AI","cs.CL"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-05-06T01:51:19Z","title":"Explaining the Effectiveness of Multi-Task Learning for Efficient Knowledge Extraction from Spine MRI Reports"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.02979","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:d6f184ad82f26510e05810edf242384ed131c97f1c43f09e5a3ac43273e189e2","target":"record","created_at":"2026-07-05T04:20:47Z","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":"286816e5914bb8931e785846da69a9f2e7cb513565735da2819988e17a885299","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-05-06T01:51:19Z","title_canon_sha256":"096cf68c08f5bc9be56cc12ad910e4de0069f2be54871638334fafd0f697d1cb"},"schema_version":"1.0","source":{"id":"2205.02979","kind":"arxiv","version":1}},"canonical_sha256":"735d1c5d38c5282475c1d20db672c53e8e41628dae7c2d58ba84df24b668df90","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"735d1c5d38c5282475c1d20db672c53e8e41628dae7c2d58ba84df24b668df90","first_computed_at":"2026-07-05T04:20:47.069888Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:20:47.069888Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"NT0PoQ8PCoUhaW71MXg8pXWh6rSOkHgjxvxV0IYLSVghRwC+q5ES4xXc/1icB6jZAcMb3bWIwrgxD6wXHwf+Cw==","signature_status":"signed_v1","signed_at":"2026-07-05T04:20:47.070374Z","signed_message":"canonical_sha256_bytes"},"source_id":"2205.02979","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d6f184ad82f26510e05810edf242384ed131c97f1c43f09e5a3ac43273e189e2","sha256:92e10279ac644f7b23d7abe8c64c375a66e7fdfdc51fdee2f7313a9ed47889d9"],"state_sha256":"93d98a2eb9161fc17b51e1b3dd86723395ad0765c1eaa5394c8ba3ad59c194f0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8VkzWjcDxsinKZmeRLHAXiZuTL1POqhgYLfi8EQ6O/eAr9sIEzE/o5oIYN7ufwK77kvks9bJjPPAW99RhIxXAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-17T07:58:24.219249Z","bundle_sha256":"22f0d01f7e463f384248a6b1185bd9714bfe7930ea65553957bacf53036eb367"}}