{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:4MOIGYH4XV56PZBDOVECIG6A4M","short_pith_number":"pith:4MOIGYH4","schema_version":"1.0","canonical_sha256":"e31c8360fcbd7be7e4237548241bc0e32ce91ee145800b58f657f20733f76f54","source":{"kind":"arxiv","id":"2210.14128","version":1},"attestation_state":"computed","paper":{"title":"IELM: An Open Information Extraction Benchmark for Pre-Trained Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Chenguang Wang, Dawn Song, Xiao Liu","submitted_at":"2022-10-25T16:25:00Z","abstract_excerpt":"We introduce a new open information extraction (OIE) benchmark for pre-trained language models (LM). Recent studies have demonstrated that pre-trained LMs, such as BERT and GPT, may store linguistic and relational knowledge. In particular, LMs are able to answer ``fill-in-the-blank'' questions when given a pre-defined relation category. Instead of focusing on pre-defined relations, we create an OIE benchmark aiming to fully examine the open relational information present in the pre-trained LMs. We accomplish this by turning pre-trained LMs into zero-shot OIE systems. Surprisingly, pre-trained "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2210.14128","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-10-25T16:25:00Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"a2b01eb25febb712dfe3d55b527655e18d16f23e41f82e41f8460f2cb47fe735","abstract_canon_sha256":"8eed95d6ff4a838423111652e79d4853ea52c30e014c7ce9075e92250fff0b7c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:10:26.421823Z","signature_b64":"msXXuJMGMD9HURausu9An/jLp3vpCYpE4l2j6Fv5wW+D3cocxou7PnrzQKx9KURSRnNH/i5yI2+xGdvDcM3kBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e31c8360fcbd7be7e4237548241bc0e32ce91ee145800b58f657f20733f76f54","last_reissued_at":"2026-07-05T05:10:26.421399Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:10:26.421399Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"IELM: An Open Information Extraction Benchmark for Pre-Trained Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Chenguang Wang, Dawn Song, Xiao Liu","submitted_at":"2022-10-25T16:25:00Z","abstract_excerpt":"We introduce a new open information extraction (OIE) benchmark for pre-trained language models (LM). Recent studies have demonstrated that pre-trained LMs, such as BERT and GPT, may store linguistic and relational knowledge. In particular, LMs are able to answer ``fill-in-the-blank'' questions when given a pre-defined relation category. Instead of focusing on pre-defined relations, we create an OIE benchmark aiming to fully examine the open relational information present in the pre-trained LMs. We accomplish this by turning pre-trained LMs into zero-shot OIE systems. Surprisingly, pre-trained "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.14128","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/2210.14128/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2210.14128","created_at":"2026-07-05T05:10:26.421452+00:00"},{"alias_kind":"arxiv_version","alias_value":"2210.14128v1","created_at":"2026-07-05T05:10:26.421452+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.14128","created_at":"2026-07-05T05:10:26.421452+00:00"},{"alias_kind":"pith_short_12","alias_value":"4MOIGYH4XV56","created_at":"2026-07-05T05:10:26.421452+00:00"},{"alias_kind":"pith_short_16","alias_value":"4MOIGYH4XV56PZBD","created_at":"2026-07-05T05:10:26.421452+00:00"},{"alias_kind":"pith_short_8","alias_value":"4MOIGYH4","created_at":"2026-07-05T05:10:26.421452+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4MOIGYH4XV56PZBDOVECIG6A4M","json":"https://pith.science/pith/4MOIGYH4XV56PZBDOVECIG6A4M.json","graph_json":"https://pith.science/api/pith-number/4MOIGYH4XV56PZBDOVECIG6A4M/graph.json","events_json":"https://pith.science/api/pith-number/4MOIGYH4XV56PZBDOVECIG6A4M/events.json","paper":"https://pith.science/paper/4MOIGYH4"},"agent_actions":{"view_html":"https://pith.science/pith/4MOIGYH4XV56PZBDOVECIG6A4M","download_json":"https://pith.science/pith/4MOIGYH4XV56PZBDOVECIG6A4M.json","view_paper":"https://pith.science/paper/4MOIGYH4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2210.14128&json=true","fetch_graph":"https://pith.science/api/pith-number/4MOIGYH4XV56PZBDOVECIG6A4M/graph.json","fetch_events":"https://pith.science/api/pith-number/4MOIGYH4XV56PZBDOVECIG6A4M/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4MOIGYH4XV56PZBDOVECIG6A4M/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4MOIGYH4XV56PZBDOVECIG6A4M/action/storage_attestation","attest_author":"https://pith.science/pith/4MOIGYH4XV56PZBDOVECIG6A4M/action/author_attestation","sign_citation":"https://pith.science/pith/4MOIGYH4XV56PZBDOVECIG6A4M/action/citation_signature","submit_replication":"https://pith.science/pith/4MOIGYH4XV56PZBDOVECIG6A4M/action/replication_record"}},"created_at":"2026-07-05T05:10:26.421452+00:00","updated_at":"2026-07-05T05:10:26.421452+00:00"}