{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:YLIYCZAJOUEUHXZ3WTWBXKGGLN","short_pith_number":"pith:YLIYCZAJ","canonical_record":{"source":{"id":"2208.01009","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-08-01T17:35:25Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"a9e4e1fe9c78d146848c10900980cf96f580bc756e07a901cc2d7aae7e52e8f5","abstract_canon_sha256":"d4aaaaab5e976616df57ff823d8c91d3aa8b1d411303634fb9571b47673d974a"},"schema_version":"1.0"},"canonical_sha256":"c2d1816409750943df3bb4ec1ba8c65b734a3963404ea2304c24c71c083abdec","source":{"kind":"arxiv","id":"2208.01009","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2208.01009","created_at":"2026-07-05T04:46:39Z"},{"alias_kind":"arxiv_version","alias_value":"2208.01009v2","created_at":"2026-07-05T04:46:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.01009","created_at":"2026-07-05T04:46:39Z"},{"alias_kind":"pith_short_12","alias_value":"YLIYCZAJOUEU","created_at":"2026-07-05T04:46:39Z"},{"alias_kind":"pith_short_16","alias_value":"YLIYCZAJOUEUHXZ3","created_at":"2026-07-05T04:46:39Z"},{"alias_kind":"pith_short_8","alias_value":"YLIYCZAJ","created_at":"2026-07-05T04:46:39Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:YLIYCZAJOUEUHXZ3WTWBXKGGLN","target":"record","payload":{"canonical_record":{"source":{"id":"2208.01009","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-08-01T17:35:25Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"a9e4e1fe9c78d146848c10900980cf96f580bc756e07a901cc2d7aae7e52e8f5","abstract_canon_sha256":"d4aaaaab5e976616df57ff823d8c91d3aa8b1d411303634fb9571b47673d974a"},"schema_version":"1.0"},"canonical_sha256":"c2d1816409750943df3bb4ec1ba8c65b734a3963404ea2304c24c71c083abdec","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:46:39.811182Z","signature_b64":"PZSOr6y0hZsT+30/+1qsYm+CL31PPpH3RPQG6ZeBmvsUipNJAuJ5tYzKGy6TwbXkCaSP8SCIvb7p4O94S6IcCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c2d1816409750943df3bb4ec1ba8c65b734a3963404ea2304c24c71c083abdec","last_reissued_at":"2026-07-05T04:46:39.810774Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:46:39.810774Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2208.01009","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":"2026-07-05T04:46:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Q3P51fkjLo6bGUiB95EtirAqDCh3kNVF3juu3elaGfPchcj5uodrmaBomVHfHROATPCNYk2VbR2U+p3b56jaAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T14:53:46.086107Z"},"content_sha256":"a38d0cc1611ebc63bcb8e61501148fe0451dbf8399072f5e4c135b88e2ad2b28","schema_version":"1.0","event_id":"sha256:a38d0cc1611ebc63bcb8e61501148fe0451dbf8399072f5e4c135b88e2ad2b28"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:YLIYCZAJOUEUHXZ3WTWBXKGGLN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Few-shot Adaptation Works with UnpredicTable Data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Ethan Perez, J\\'er\\'emy Scheurer, Jonathan Jao, Jun Shern Chan, Michael Pieler","submitted_at":"2022-08-01T17:35:25Z","abstract_excerpt":"Prior work on language models (LMs) shows that training on a large number of diverse tasks improves few-shot learning (FSL) performance on new tasks. We take this to the extreme, automatically extracting 413,299 tasks from internet tables - orders of magnitude more than the next-largest public datasets. Finetuning on the resulting dataset leads to improved FSL performance on Natural Language Processing (NLP) tasks, but not proportionally to dataset scale. In fact, we find that narrow subsets of our dataset sometimes outperform more diverse datasets. For example, finetuning on software document"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.01009","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/2208.01009/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":"2026-07-05T04:46:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fiIsuPX9wS5efX2vrX9sbGMsoCUS/oaTK++ob7nNW8CzmFlOFx6xR11rT5jO9XNlsw7IHl65S3LJLO5kzn7ACQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T14:53:46.086513Z"},"content_sha256":"a77d35329f15b96e605776240171c7fd80ae660823be7a68a18c03fab7969226","schema_version":"1.0","event_id":"sha256:a77d35329f15b96e605776240171c7fd80ae660823be7a68a18c03fab7969226"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YLIYCZAJOUEUHXZ3WTWBXKGGLN/bundle.json","state_url":"https://pith.science/pith/YLIYCZAJOUEUHXZ3WTWBXKGGLN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YLIYCZAJOUEUHXZ3WTWBXKGGLN/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":"2026-07-07T14:53:46Z","links":{"resolver":"https://pith.science/pith/YLIYCZAJOUEUHXZ3WTWBXKGGLN","bundle":"https://pith.science/pith/YLIYCZAJOUEUHXZ3WTWBXKGGLN/bundle.json","state":"https://pith.science/pith/YLIYCZAJOUEUHXZ3WTWBXKGGLN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YLIYCZAJOUEUHXZ3WTWBXKGGLN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:YLIYCZAJOUEUHXZ3WTWBXKGGLN","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":"d4aaaaab5e976616df57ff823d8c91d3aa8b1d411303634fb9571b47673d974a","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-08-01T17:35:25Z","title_canon_sha256":"a9e4e1fe9c78d146848c10900980cf96f580bc756e07a901cc2d7aae7e52e8f5"},"schema_version":"1.0","source":{"id":"2208.01009","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2208.01009","created_at":"2026-07-05T04:46:39Z"},{"alias_kind":"arxiv_version","alias_value":"2208.01009v2","created_at":"2026-07-05T04:46:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.01009","created_at":"2026-07-05T04:46:39Z"},{"alias_kind":"pith_short_12","alias_value":"YLIYCZAJOUEU","created_at":"2026-07-05T04:46:39Z"},{"alias_kind":"pith_short_16","alias_value":"YLIYCZAJOUEUHXZ3","created_at":"2026-07-05T04:46:39Z"},{"alias_kind":"pith_short_8","alias_value":"YLIYCZAJ","created_at":"2026-07-05T04:46:39Z"}],"graph_snapshots":[{"event_id":"sha256:a77d35329f15b96e605776240171c7fd80ae660823be7a68a18c03fab7969226","target":"graph","created_at":"2026-07-05T04:46:39Z","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/2208.01009/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Prior work on language models (LMs) shows that training on a large number of diverse tasks improves few-shot learning (FSL) performance on new tasks. We take this to the extreme, automatically extracting 413,299 tasks from internet tables - orders of magnitude more than the next-largest public datasets. Finetuning on the resulting dataset leads to improved FSL performance on Natural Language Processing (NLP) tasks, but not proportionally to dataset scale. In fact, we find that narrow subsets of our dataset sometimes outperform more diverse datasets. For example, finetuning on software document","authors_text":"Ethan Perez, J\\'er\\'emy Scheurer, Jonathan Jao, Jun Shern Chan, Michael Pieler","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-08-01T17:35:25Z","title":"Few-shot Adaptation Works with UnpredicTable Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.01009","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:a38d0cc1611ebc63bcb8e61501148fe0451dbf8399072f5e4c135b88e2ad2b28","target":"record","created_at":"2026-07-05T04:46:39Z","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":"d4aaaaab5e976616df57ff823d8c91d3aa8b1d411303634fb9571b47673d974a","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-08-01T17:35:25Z","title_canon_sha256":"a9e4e1fe9c78d146848c10900980cf96f580bc756e07a901cc2d7aae7e52e8f5"},"schema_version":"1.0","source":{"id":"2208.01009","kind":"arxiv","version":2}},"canonical_sha256":"c2d1816409750943df3bb4ec1ba8c65b734a3963404ea2304c24c71c083abdec","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c2d1816409750943df3bb4ec1ba8c65b734a3963404ea2304c24c71c083abdec","first_computed_at":"2026-07-05T04:46:39.810774Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:46:39.810774Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"PZSOr6y0hZsT+30/+1qsYm+CL31PPpH3RPQG6ZeBmvsUipNJAuJ5tYzKGy6TwbXkCaSP8SCIvb7p4O94S6IcCg==","signature_status":"signed_v1","signed_at":"2026-07-05T04:46:39.811182Z","signed_message":"canonical_sha256_bytes"},"source_id":"2208.01009","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a38d0cc1611ebc63bcb8e61501148fe0451dbf8399072f5e4c135b88e2ad2b28","sha256:a77d35329f15b96e605776240171c7fd80ae660823be7a68a18c03fab7969226"],"state_sha256":"9c8c4aa3479318681c8fb7bb54a7fb2452259ec7c8a043fb77a78117dccc26a3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1zKqDPXIZWzs037bNCJPhE0pqNkC9fQeg6HvSCVCwZ7VyhUlBIIvhZvXpSteLK3uN+bcoPWRfwJ6AHd9wtmXCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T14:53:46.088524Z","bundle_sha256":"b1c9b9999273316a6ddb173667b1955054f0f4c555dfc57e398840344eba7c1b"}}