{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:7K5PEPEY3SG2XP7FS73FYJA4M3","short_pith_number":"pith:7K5PEPEY","schema_version":"1.0","canonical_sha256":"fabaf23c98dc8dabbfe597f65c241c66f4cbf8a900874bb86c49c2e9fb6a0dfd","source":{"kind":"arxiv","id":"2110.07038","version":2},"attestation_state":"computed","paper":{"title":"Towards Efficient NLP: A Standard Evaluation and A Strong Baseline","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Hao Jiang, Jiawen Wu, Junliang He, Lingling Wu, Tianxiang Sun, Xiangyang Liu, Xinyu Zhang, Xipeng Qiu, Xuanjing Huang, Zhao Cao","submitted_at":"2021-10-13T21:17:15Z","abstract_excerpt":"Supersized pre-trained language models have pushed the accuracy of various natural language processing (NLP) tasks to a new state-of-the-art (SOTA). Rather than pursuing the reachless SOTA accuracy, more and more researchers start paying attention on model efficiency and usability. Different from accuracy, the metric for efficiency varies across different studies, making them hard to be fairly compared. To that end, this work presents ELUE (Efficient Language Understanding Evaluation), a standard evaluation, and a public leaderboard for efficient NLP models. ELUE is dedicated to depict the Par"},"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":"2110.07038","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-10-13T21:17:15Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"d8884857c51f0873415cdfd0c5efe0b1c5a457fd1c1c47b9d3c5c9b9a4ebf3a2","abstract_canon_sha256":"350ae22c22c37fd3604ed187f5d0945265a3bcc8948605e4081c3cddd81ba853"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:12:50.987494Z","signature_b64":"AYMfqAGRaZtt4zvI1EWup4b6ohnjbxU4y1kgIKAaCuaUG2eoXugCKunl+fSVXHvCTSp7rKNwgkRmOjVxyybLCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fabaf23c98dc8dabbfe597f65c241c66f4cbf8a900874bb86c49c2e9fb6a0dfd","last_reissued_at":"2026-07-05T04:12:50.987098Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:12:50.987098Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards Efficient NLP: A Standard Evaluation and A Strong Baseline","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Hao Jiang, Jiawen Wu, Junliang He, Lingling Wu, Tianxiang Sun, Xiangyang Liu, Xinyu Zhang, Xipeng Qiu, Xuanjing Huang, Zhao Cao","submitted_at":"2021-10-13T21:17:15Z","abstract_excerpt":"Supersized pre-trained language models have pushed the accuracy of various natural language processing (NLP) tasks to a new state-of-the-art (SOTA). Rather than pursuing the reachless SOTA accuracy, more and more researchers start paying attention on model efficiency and usability. Different from accuracy, the metric for efficiency varies across different studies, making them hard to be fairly compared. To that end, this work presents ELUE (Efficient Language Understanding Evaluation), a standard evaluation, and a public leaderboard for efficient NLP models. ELUE is dedicated to depict the Par"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.07038","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/2110.07038/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":"2110.07038","created_at":"2026-07-05T04:12:50.987154+00:00"},{"alias_kind":"arxiv_version","alias_value":"2110.07038v2","created_at":"2026-07-05T04:12:50.987154+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.07038","created_at":"2026-07-05T04:12:50.987154+00:00"},{"alias_kind":"pith_short_12","alias_value":"7K5PEPEY3SG2","created_at":"2026-07-05T04:12:50.987154+00:00"},{"alias_kind":"pith_short_16","alias_value":"7K5PEPEY3SG2XP7F","created_at":"2026-07-05T04:12:50.987154+00:00"},{"alias_kind":"pith_short_8","alias_value":"7K5PEPEY","created_at":"2026-07-05T04:12:50.987154+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/7K5PEPEY3SG2XP7FS73FYJA4M3","json":"https://pith.science/pith/7K5PEPEY3SG2XP7FS73FYJA4M3.json","graph_json":"https://pith.science/api/pith-number/7K5PEPEY3SG2XP7FS73FYJA4M3/graph.json","events_json":"https://pith.science/api/pith-number/7K5PEPEY3SG2XP7FS73FYJA4M3/events.json","paper":"https://pith.science/paper/7K5PEPEY"},"agent_actions":{"view_html":"https://pith.science/pith/7K5PEPEY3SG2XP7FS73FYJA4M3","download_json":"https://pith.science/pith/7K5PEPEY3SG2XP7FS73FYJA4M3.json","view_paper":"https://pith.science/paper/7K5PEPEY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2110.07038&json=true","fetch_graph":"https://pith.science/api/pith-number/7K5PEPEY3SG2XP7FS73FYJA4M3/graph.json","fetch_events":"https://pith.science/api/pith-number/7K5PEPEY3SG2XP7FS73FYJA4M3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7K5PEPEY3SG2XP7FS73FYJA4M3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7K5PEPEY3SG2XP7FS73FYJA4M3/action/storage_attestation","attest_author":"https://pith.science/pith/7K5PEPEY3SG2XP7FS73FYJA4M3/action/author_attestation","sign_citation":"https://pith.science/pith/7K5PEPEY3SG2XP7FS73FYJA4M3/action/citation_signature","submit_replication":"https://pith.science/pith/7K5PEPEY3SG2XP7FS73FYJA4M3/action/replication_record"}},"created_at":"2026-07-05T04:12:50.987154+00:00","updated_at":"2026-07-05T04:12:50.987154+00:00"}