{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:4X2I7Z6ADMJI3Z4IAUSZUHFVIL","short_pith_number":"pith:4X2I7Z6A","canonical_record":{"source":{"id":"2305.01651","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-05-02T17:59:46Z","cross_cats_sorted":[],"title_canon_sha256":"5b2afc49d013fdc0eacca6c30b84e8cb906b6651374d7fead64a9362c0aef8b8","abstract_canon_sha256":"08b1af8fc7d48fc52ef70758147914180fa2ec5f8ecdc928e197ce347b58677b"},"schema_version":"1.0"},"canonical_sha256":"e5f48fe7c01b128de78805259a1cb542e89d7de4218e2855fb3837e6755037b1","source":{"kind":"arxiv","id":"2305.01651","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.01651","created_at":"2026-07-05T06:06:27Z"},{"alias_kind":"arxiv_version","alias_value":"2305.01651v1","created_at":"2026-07-05T06:06:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.01651","created_at":"2026-07-05T06:06:27Z"},{"alias_kind":"pith_short_12","alias_value":"4X2I7Z6ADMJI","created_at":"2026-07-05T06:06:27Z"},{"alias_kind":"pith_short_16","alias_value":"4X2I7Z6ADMJI3Z4I","created_at":"2026-07-05T06:06:27Z"},{"alias_kind":"pith_short_8","alias_value":"4X2I7Z6A","created_at":"2026-07-05T06:06:27Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:4X2I7Z6ADMJI3Z4IAUSZUHFVIL","target":"record","payload":{"canonical_record":{"source":{"id":"2305.01651","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-05-02T17:59:46Z","cross_cats_sorted":[],"title_canon_sha256":"5b2afc49d013fdc0eacca6c30b84e8cb906b6651374d7fead64a9362c0aef8b8","abstract_canon_sha256":"08b1af8fc7d48fc52ef70758147914180fa2ec5f8ecdc928e197ce347b58677b"},"schema_version":"1.0"},"canonical_sha256":"e5f48fe7c01b128de78805259a1cb542e89d7de4218e2855fb3837e6755037b1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:06:27.967681Z","signature_b64":"AWHbW5+TjdtV3hbOzBZ68Ym72nGOoK8OdAVB8OQICAM2lbdypy+M4ElZ22NbQ6gCnUT4V6x2q3J1dAnAD7SaCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e5f48fe7c01b128de78805259a1cb542e89d7de4218e2855fb3837e6755037b1","last_reissued_at":"2026-07-05T06:06:27.967304Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:06:27.967304Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2305.01651","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":"2026-07-05T06:06:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FMlmgYNoA8DAPlRsZ8Jvt7Tk4yVxBHrZdQIxR6vhG3dTtGfeaK30UboT3Eb8niOF6X4dvReI22JYEtOmbfAxAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T20:58:08.435784Z"},"content_sha256":"5c04fde02447c585a6bc783b158421e876f591f5c521fe2862fa759bf8850c43","schema_version":"1.0","event_id":"sha256:5c04fde02447c585a6bc783b158421e876f591f5c521fe2862fa759bf8850c43"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:4X2I7Z6ADMJI3Z4IAUSZUHFVIL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Can LMs Learn New Entities from Descriptions? Challenges in Propagating Injected Knowledge","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Eunsol Choi, Greg Durrett, Michael J.Q. Zhang, Shankar Padmanabhan, Yasumasa Onoe","submitted_at":"2023-05-02T17:59:46Z","abstract_excerpt":"Pre-trained language models (LMs) are used for knowledge intensive tasks like question answering, but their knowledge gets continuously outdated as the world changes. Prior work has studied targeted updates to LMs, injecting individual facts and evaluating whether the model learns these facts while not changing predictions on other contexts. We take a step forward and study LMs' abilities to make inferences based on injected facts (or propagate those facts): for example, after learning that something is a TV show, does an LM predict that you can watch it? We study this with two cloze-style tas"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.01651","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/2305.01651/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":"2026-07-05T06:06:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KmiCRTS7wTSeI6KFdNHAkmaxIcXg+pC+IOqUaBroAeisPHY/Iny0a0uWD9QHc4CFeHDm995FtxfgtEe//CHwDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T20:58:08.436311Z"},"content_sha256":"a9d8abb70466fb88253dcc849df01b7f5087b293a008ad6c663c7c74623aa028","schema_version":"1.0","event_id":"sha256:a9d8abb70466fb88253dcc849df01b7f5087b293a008ad6c663c7c74623aa028"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4X2I7Z6ADMJI3Z4IAUSZUHFVIL/bundle.json","state_url":"https://pith.science/pith/4X2I7Z6ADMJI3Z4IAUSZUHFVIL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4X2I7Z6ADMJI3Z4IAUSZUHFVIL/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":"2026-08-08T20:58:08Z","links":{"resolver":"https://pith.science/pith/4X2I7Z6ADMJI3Z4IAUSZUHFVIL","bundle":"https://pith.science/pith/4X2I7Z6ADMJI3Z4IAUSZUHFVIL/bundle.json","state":"https://pith.science/pith/4X2I7Z6ADMJI3Z4IAUSZUHFVIL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4X2I7Z6ADMJI3Z4IAUSZUHFVIL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:4X2I7Z6ADMJI3Z4IAUSZUHFVIL","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":"08b1af8fc7d48fc52ef70758147914180fa2ec5f8ecdc928e197ce347b58677b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-05-02T17:59:46Z","title_canon_sha256":"5b2afc49d013fdc0eacca6c30b84e8cb906b6651374d7fead64a9362c0aef8b8"},"schema_version":"1.0","source":{"id":"2305.01651","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.01651","created_at":"2026-07-05T06:06:27Z"},{"alias_kind":"arxiv_version","alias_value":"2305.01651v1","created_at":"2026-07-05T06:06:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.01651","created_at":"2026-07-05T06:06:27Z"},{"alias_kind":"pith_short_12","alias_value":"4X2I7Z6ADMJI","created_at":"2026-07-05T06:06:27Z"},{"alias_kind":"pith_short_16","alias_value":"4X2I7Z6ADMJI3Z4I","created_at":"2026-07-05T06:06:27Z"},{"alias_kind":"pith_short_8","alias_value":"4X2I7Z6A","created_at":"2026-07-05T06:06:27Z"}],"graph_snapshots":[{"event_id":"sha256:a9d8abb70466fb88253dcc849df01b7f5087b293a008ad6c663c7c74623aa028","target":"graph","created_at":"2026-07-05T06:06:27Z","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/2305.01651/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Pre-trained language models (LMs) are used for knowledge intensive tasks like question answering, but their knowledge gets continuously outdated as the world changes. Prior work has studied targeted updates to LMs, injecting individual facts and evaluating whether the model learns these facts while not changing predictions on other contexts. We take a step forward and study LMs' abilities to make inferences based on injected facts (or propagate those facts): for example, after learning that something is a TV show, does an LM predict that you can watch it? We study this with two cloze-style tas","authors_text":"Eunsol Choi, Greg Durrett, Michael J.Q. Zhang, Shankar Padmanabhan, Yasumasa Onoe","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-05-02T17:59:46Z","title":"Can LMs Learn New Entities from Descriptions? Challenges in Propagating Injected Knowledge"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.01651","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":"sha256:5c04fde02447c585a6bc783b158421e876f591f5c521fe2862fa759bf8850c43","target":"record","created_at":"2026-07-05T06:06:27Z","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":"08b1af8fc7d48fc52ef70758147914180fa2ec5f8ecdc928e197ce347b58677b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-05-02T17:59:46Z","title_canon_sha256":"5b2afc49d013fdc0eacca6c30b84e8cb906b6651374d7fead64a9362c0aef8b8"},"schema_version":"1.0","source":{"id":"2305.01651","kind":"arxiv","version":1}},"canonical_sha256":"e5f48fe7c01b128de78805259a1cb542e89d7de4218e2855fb3837e6755037b1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e5f48fe7c01b128de78805259a1cb542e89d7de4218e2855fb3837e6755037b1","first_computed_at":"2026-07-05T06:06:27.967304Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:06:27.967304Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"AWHbW5+TjdtV3hbOzBZ68Ym72nGOoK8OdAVB8OQICAM2lbdypy+M4ElZ22NbQ6gCnUT4V6x2q3J1dAnAD7SaCw==","signature_status":"signed_v1","signed_at":"2026-07-05T06:06:27.967681Z","signed_message":"canonical_sha256_bytes"},"source_id":"2305.01651","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5c04fde02447c585a6bc783b158421e876f591f5c521fe2862fa759bf8850c43","sha256:a9d8abb70466fb88253dcc849df01b7f5087b293a008ad6c663c7c74623aa028"],"state_sha256":"cde8ba26242241b0102a1839f4ae22cbaf387a691ff002666d1fc82b7c0b4ebf"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3vh/mRezt2ZwIK02k1P3eT4o33FFwdWhz+KBlNKKm7iLVtZOT5hXvcZc9gRfLsTMX7jxK/V3hetbKrucMnCRAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T20:58:08.439885Z","bundle_sha256":"281cb2d6dbeb5fefa6ccdd743ca0db56e08cda10f760859e0324b583dc13d38b"}}