{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:LBGKPL63XGV4Q443YG3KL2YEJY","short_pith_number":"pith:LBGKPL63","schema_version":"1.0","canonical_sha256":"584ca7afdbb9abc8739bc1b6a5eb044e2fbe3663a85c35771c0344476c413310","source":{"kind":"arxiv","id":"2210.05159","version":2},"attestation_state":"computed","paper":{"title":"Can Language Models Be Specific? How?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Jie Huang, Jinjun Xiong, Kevin Chen-Chuan Chang, Wen-Mei Hwu","submitted_at":"2022-10-11T05:38:27Z","abstract_excerpt":"\"He is a person\", \"Paris is located on the earth\". Both statements are correct but meaningless - due to lack of specificity. In this paper, we propose to measure how specific the language of pre-trained language models (PLMs) is. To achieve this, we introduce a novel approach to build a benchmark for specificity testing by forming masked token prediction tasks with prompts. For instance, given \"Toronto is located in [MASK].\", we want to test whether a more specific answer will be better filled in by PLMs, e.g., Ontario instead of Canada. From our evaluations, we show that existing PLMs have on"},"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":"2210.05159","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-10-11T05:38:27Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"d1582598e775226db8d84719dae65237cd2ecc5db269118faffbe1e7eaab3f1b","abstract_canon_sha256":"f24cf25c3a1d0e91d5d924b7ed271b40bd96eb522ba8e32e48a378082008d2cf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:14:05.592747Z","signature_b64":"LMhWfb1erVsukuzvBAl/SMODY6lve8Q9Pr5zMlBXUjsd3P3IV4TOrOcEV+zebI8l4Am3z6ZemhBwqUD+UNGWDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"584ca7afdbb9abc8739bc1b6a5eb044e2fbe3663a85c35771c0344476c413310","last_reissued_at":"2026-07-05T06:14:05.592312Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:14:05.592312Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Can Language Models Be Specific? How?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Jie Huang, Jinjun Xiong, Kevin Chen-Chuan Chang, Wen-Mei Hwu","submitted_at":"2022-10-11T05:38:27Z","abstract_excerpt":"\"He is a person\", \"Paris is located on the earth\". Both statements are correct but meaningless - due to lack of specificity. In this paper, we propose to measure how specific the language of pre-trained language models (PLMs) is. To achieve this, we introduce a novel approach to build a benchmark for specificity testing by forming masked token prediction tasks with prompts. For instance, given \"Toronto is located in [MASK].\", we want to test whether a more specific answer will be better filled in by PLMs, e.g., Ontario instead of Canada. From our evaluations, we show that existing PLMs have on"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.05159","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/2210.05159/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":"2210.05159","created_at":"2026-07-05T06:14:05.592381+00:00"},{"alias_kind":"arxiv_version","alias_value":"2210.05159v2","created_at":"2026-07-05T06:14:05.592381+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.05159","created_at":"2026-07-05T06:14:05.592381+00:00"},{"alias_kind":"pith_short_12","alias_value":"LBGKPL63XGV4","created_at":"2026-07-05T06:14:05.592381+00:00"},{"alias_kind":"pith_short_16","alias_value":"LBGKPL63XGV4Q443","created_at":"2026-07-05T06:14:05.592381+00:00"},{"alias_kind":"pith_short_8","alias_value":"LBGKPL63","created_at":"2026-07-05T06:14:05.592381+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/LBGKPL63XGV4Q443YG3KL2YEJY","json":"https://pith.science/pith/LBGKPL63XGV4Q443YG3KL2YEJY.json","graph_json":"https://pith.science/api/pith-number/LBGKPL63XGV4Q443YG3KL2YEJY/graph.json","events_json":"https://pith.science/api/pith-number/LBGKPL63XGV4Q443YG3KL2YEJY/events.json","paper":"https://pith.science/paper/LBGKPL63"},"agent_actions":{"view_html":"https://pith.science/pith/LBGKPL63XGV4Q443YG3KL2YEJY","download_json":"https://pith.science/pith/LBGKPL63XGV4Q443YG3KL2YEJY.json","view_paper":"https://pith.science/paper/LBGKPL63","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2210.05159&json=true","fetch_graph":"https://pith.science/api/pith-number/LBGKPL63XGV4Q443YG3KL2YEJY/graph.json","fetch_events":"https://pith.science/api/pith-number/LBGKPL63XGV4Q443YG3KL2YEJY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LBGKPL63XGV4Q443YG3KL2YEJY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LBGKPL63XGV4Q443YG3KL2YEJY/action/storage_attestation","attest_author":"https://pith.science/pith/LBGKPL63XGV4Q443YG3KL2YEJY/action/author_attestation","sign_citation":"https://pith.science/pith/LBGKPL63XGV4Q443YG3KL2YEJY/action/citation_signature","submit_replication":"https://pith.science/pith/LBGKPL63XGV4Q443YG3KL2YEJY/action/replication_record"}},"created_at":"2026-07-05T06:14:05.592381+00:00","updated_at":"2026-07-05T06:14:05.592381+00:00"}