{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:QOBBTKUHV7BJKWXD2DAWTT67ID","short_pith_number":"pith:QOBBTKUH","canonical_record":{"source":{"id":"1909.10705","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-09-24T04:26:27Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"4b41d265570e94feff43786f22febf808c9fa280deea7026ce4193452db98d2d","abstract_canon_sha256":"c9b9329946d49a41c5a0d139bcd799bdc9bb233975a2e4a425a976ef5408c77a"},"schema_version":"1.0"},"canonical_sha256":"838219aa87afc2955ae3d0c169cfdf40c5638111d6854505b79cba6bbaf8c61f","source":{"kind":"arxiv","id":"1909.10705","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1909.10705","created_at":"2026-07-05T00:07:01Z"},{"alias_kind":"arxiv_version","alias_value":"1909.10705v1","created_at":"2026-07-05T00:07:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.10705","created_at":"2026-07-05T00:07:01Z"},{"alias_kind":"pith_short_12","alias_value":"QOBBTKUHV7BJ","created_at":"2026-07-05T00:07:01Z"},{"alias_kind":"pith_short_16","alias_value":"QOBBTKUHV7BJKWXD","created_at":"2026-07-05T00:07:01Z"},{"alias_kind":"pith_short_8","alias_value":"QOBBTKUH","created_at":"2026-07-05T00:07:01Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:QOBBTKUHV7BJKWXD2DAWTT67ID","target":"record","payload":{"canonical_record":{"source":{"id":"1909.10705","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-09-24T04:26:27Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"4b41d265570e94feff43786f22febf808c9fa280deea7026ce4193452db98d2d","abstract_canon_sha256":"c9b9329946d49a41c5a0d139bcd799bdc9bb233975a2e4a425a976ef5408c77a"},"schema_version":"1.0"},"canonical_sha256":"838219aa87afc2955ae3d0c169cfdf40c5638111d6854505b79cba6bbaf8c61f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:07:01.300433Z","signature_b64":"LJj4rrAB8YJzVck60yOMOcSr0PTjp2FDwMuK1T8o9tjfbRynBakdEDT2tA4/W6w56ni0P/7ONGkG8WBtyZUtCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"838219aa87afc2955ae3d0c169cfdf40c5638111d6854505b79cba6bbaf8c61f","last_reissued_at":"2026-07-05T00:07:01.299936Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:07:01.299936Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1909.10705","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":"2026-07-05T00:07:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bmnQqiBYa0oo2A2j3ez2OtH1baHDBX1afeJ2ZdWcRek53HO4O00D35aD2Lcy1f1zYL2wTlSksXVqCJTHzOMoAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T07:43:54.072903Z"},"content_sha256":"c5970ecb82236fcabe51c979f8bababf111a7861454d09e1b71785a97144510a","schema_version":"1.0","event_id":"sha256:c5970ecb82236fcabe51c979f8bababf111a7861454d09e1b71785a97144510a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:QOBBTKUHV7BJKWXD2DAWTT67ID","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Do Massively Pretrained Language Models Make Better Storytellers?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Abigail See, Akhila Yerukola, Aneesh Pappu, Christopher D. Manning, Rohun Saxena","submitted_at":"2019-09-24T04:26:27Z","abstract_excerpt":"Large neural language models trained on massive amounts of text have emerged as a formidable strategy for Natural Language Understanding tasks. However, the strength of these models as Natural Language Generators is less clear. Though anecdotal evidence suggests that these models generate better quality text, there has been no detailed study characterizing their generation abilities. In this work, we compare the performance of an extensively pretrained model, OpenAI GPT2-117 (Radford et al., 2019), to a state-of-the-art neural story generation model (Fan et al., 2018). By evaluating the genera"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.10705","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/1909.10705/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":"2026-07-05T00:07:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"auez7yxQQGYMCkGiJ6RU4ibeafFV/w6MO27zcuUDKysV8QqciBjsoZb09eRoPLr44sBZ44jNGCXHNbmZpNzoCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T07:43:54.073394Z"},"content_sha256":"470a2789515e54eb062efd11763492ed0766a466eec515e5ab134fda62b52e30","schema_version":"1.0","event_id":"sha256:470a2789515e54eb062efd11763492ed0766a466eec515e5ab134fda62b52e30"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QOBBTKUHV7BJKWXD2DAWTT67ID/bundle.json","state_url":"https://pith.science/pith/QOBBTKUHV7BJKWXD2DAWTT67ID/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QOBBTKUHV7BJKWXD2DAWTT67ID/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":"2026-08-19T07:43:54Z","links":{"resolver":"https://pith.science/pith/QOBBTKUHV7BJKWXD2DAWTT67ID","bundle":"https://pith.science/pith/QOBBTKUHV7BJKWXD2DAWTT67ID/bundle.json","state":"https://pith.science/pith/QOBBTKUHV7BJKWXD2DAWTT67ID/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QOBBTKUHV7BJKWXD2DAWTT67ID/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:QOBBTKUHV7BJKWXD2DAWTT67ID","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":"c9b9329946d49a41c5a0d139bcd799bdc9bb233975a2e4a425a976ef5408c77a","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-09-24T04:26:27Z","title_canon_sha256":"4b41d265570e94feff43786f22febf808c9fa280deea7026ce4193452db98d2d"},"schema_version":"1.0","source":{"id":"1909.10705","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1909.10705","created_at":"2026-07-05T00:07:01Z"},{"alias_kind":"arxiv_version","alias_value":"1909.10705v1","created_at":"2026-07-05T00:07:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.10705","created_at":"2026-07-05T00:07:01Z"},{"alias_kind":"pith_short_12","alias_value":"QOBBTKUHV7BJ","created_at":"2026-07-05T00:07:01Z"},{"alias_kind":"pith_short_16","alias_value":"QOBBTKUHV7BJKWXD","created_at":"2026-07-05T00:07:01Z"},{"alias_kind":"pith_short_8","alias_value":"QOBBTKUH","created_at":"2026-07-05T00:07:01Z"}],"graph_snapshots":[{"event_id":"sha256:470a2789515e54eb062efd11763492ed0766a466eec515e5ab134fda62b52e30","target":"graph","created_at":"2026-07-05T00:07:01Z","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/1909.10705/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large neural language models trained on massive amounts of text have emerged as a formidable strategy for Natural Language Understanding tasks. However, the strength of these models as Natural Language Generators is less clear. Though anecdotal evidence suggests that these models generate better quality text, there has been no detailed study characterizing their generation abilities. In this work, we compare the performance of an extensively pretrained model, OpenAI GPT2-117 (Radford et al., 2019), to a state-of-the-art neural story generation model (Fan et al., 2018). By evaluating the genera","authors_text":"Abigail See, Akhila Yerukola, Aneesh Pappu, Christopher D. Manning, Rohun Saxena","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-09-24T04:26:27Z","title":"Do Massively Pretrained Language Models Make Better Storytellers?"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.10705","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:c5970ecb82236fcabe51c979f8bababf111a7861454d09e1b71785a97144510a","target":"record","created_at":"2026-07-05T00:07:01Z","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":"c9b9329946d49a41c5a0d139bcd799bdc9bb233975a2e4a425a976ef5408c77a","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-09-24T04:26:27Z","title_canon_sha256":"4b41d265570e94feff43786f22febf808c9fa280deea7026ce4193452db98d2d"},"schema_version":"1.0","source":{"id":"1909.10705","kind":"arxiv","version":1}},"canonical_sha256":"838219aa87afc2955ae3d0c169cfdf40c5638111d6854505b79cba6bbaf8c61f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"838219aa87afc2955ae3d0c169cfdf40c5638111d6854505b79cba6bbaf8c61f","first_computed_at":"2026-07-05T00:07:01.299936Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:07:01.299936Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"LJj4rrAB8YJzVck60yOMOcSr0PTjp2FDwMuK1T8o9tjfbRynBakdEDT2tA4/W6w56ni0P/7ONGkG8WBtyZUtCg==","signature_status":"signed_v1","signed_at":"2026-07-05T00:07:01.300433Z","signed_message":"canonical_sha256_bytes"},"source_id":"1909.10705","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c5970ecb82236fcabe51c979f8bababf111a7861454d09e1b71785a97144510a","sha256:470a2789515e54eb062efd11763492ed0766a466eec515e5ab134fda62b52e30"],"state_sha256":"04c4c462beac07e6084c0793f7d8bc2dbf49928943aeb6dafbcd11c1a043e57d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VvKJaNkNUinP3SrTXv2hlIv5hStxVgSsc6L3ubmhPMe1CJfgdQ3TVfPbRCn3udHIec3s9a4KXUjsxDI4fnxPBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-19T07:43:54.079167Z","bundle_sha256":"5d2a3237aa3d6c77f514c903f80a63cf7f57b76a6f019bc9dcea6cd63eadbe7f"}}