{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:4WA5YK2COAVBW3MKOHRSTSOQPT","short_pith_number":"pith:4WA5YK2C","schema_version":"1.0","canonical_sha256":"e581dc2b42702a1b6d8a71e329c9d07cd09f8f18af62329561bb9d987a075874","source":{"kind":"arxiv","id":"2311.08287","version":1},"attestation_state":"computed","paper":{"title":"How Well Do Large Language Models Understand Syntax? An Evaluation by Asking Natural Language Questions","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Houquan Zhou, Min Zhang, Xinyu Duan, Xuebin Wang, Yang Hou, Zhefeng Wang, Zhenghua Li","submitted_at":"2023-11-14T16:30:36Z","abstract_excerpt":"While recent advancements in large language models (LLMs) bring us closer to achieving artificial general intelligence, the question persists: Do LLMs truly understand language, or do they merely mimic comprehension through pattern recognition? This study seeks to explore this question through the lens of syntax, a crucial component of sentence comprehension. Adopting a natural language question-answering (Q&A) scheme, we craft questions targeting nine syntactic knowledge points that are most closely related to sentence comprehension. Experiments conducted on 24 LLMs suggest that most have a l"},"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":"2311.08287","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-11-14T16:30:36Z","cross_cats_sorted":[],"title_canon_sha256":"528c8e858e057a386e4e727c8e9f103e59880595d73e46f13ed42a46e300eee2","abstract_canon_sha256":"5ead1d0f2270a8902f811cdf321d299a3e2519835aa9f4a6587f7b603e6929fe"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:12:43.331581Z","signature_b64":"jXUAOlaZ231DnzEseOXxcLReULhxyNzPLxv+llDIvoQyRyNsn/nc/pZiwQHP5x5i2S9QezsBCqLOWEvnrbtWBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e581dc2b42702a1b6d8a71e329c9d07cd09f8f18af62329561bb9d987a075874","last_reissued_at":"2026-07-05T07:12:43.331192Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:12:43.331192Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"How Well Do Large Language Models Understand Syntax? An Evaluation by Asking Natural Language Questions","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Houquan Zhou, Min Zhang, Xinyu Duan, Xuebin Wang, Yang Hou, Zhefeng Wang, Zhenghua Li","submitted_at":"2023-11-14T16:30:36Z","abstract_excerpt":"While recent advancements in large language models (LLMs) bring us closer to achieving artificial general intelligence, the question persists: Do LLMs truly understand language, or do they merely mimic comprehension through pattern recognition? This study seeks to explore this question through the lens of syntax, a crucial component of sentence comprehension. Adopting a natural language question-answering (Q&A) scheme, we craft questions targeting nine syntactic knowledge points that are most closely related to sentence comprehension. Experiments conducted on 24 LLMs suggest that most have a l"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.08287","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/2311.08287/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":"2311.08287","created_at":"2026-07-05T07:12:43.331254+00:00"},{"alias_kind":"arxiv_version","alias_value":"2311.08287v1","created_at":"2026-07-05T07:12:43.331254+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.08287","created_at":"2026-07-05T07:12:43.331254+00:00"},{"alias_kind":"pith_short_12","alias_value":"4WA5YK2COAVB","created_at":"2026-07-05T07:12:43.331254+00:00"},{"alias_kind":"pith_short_16","alias_value":"4WA5YK2COAVBW3MK","created_at":"2026-07-05T07:12:43.331254+00:00"},{"alias_kind":"pith_short_8","alias_value":"4WA5YK2C","created_at":"2026-07-05T07:12:43.331254+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.21299","citing_title":"Tracing the ongoing emergence of human-like reasoning in Large Language Models","ref_index":21,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4WA5YK2COAVBW3MKOHRSTSOQPT","json":"https://pith.science/pith/4WA5YK2COAVBW3MKOHRSTSOQPT.json","graph_json":"https://pith.science/api/pith-number/4WA5YK2COAVBW3MKOHRSTSOQPT/graph.json","events_json":"https://pith.science/api/pith-number/4WA5YK2COAVBW3MKOHRSTSOQPT/events.json","paper":"https://pith.science/paper/4WA5YK2C"},"agent_actions":{"view_html":"https://pith.science/pith/4WA5YK2COAVBW3MKOHRSTSOQPT","download_json":"https://pith.science/pith/4WA5YK2COAVBW3MKOHRSTSOQPT.json","view_paper":"https://pith.science/paper/4WA5YK2C","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2311.08287&json=true","fetch_graph":"https://pith.science/api/pith-number/4WA5YK2COAVBW3MKOHRSTSOQPT/graph.json","fetch_events":"https://pith.science/api/pith-number/4WA5YK2COAVBW3MKOHRSTSOQPT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4WA5YK2COAVBW3MKOHRSTSOQPT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4WA5YK2COAVBW3MKOHRSTSOQPT/action/storage_attestation","attest_author":"https://pith.science/pith/4WA5YK2COAVBW3MKOHRSTSOQPT/action/author_attestation","sign_citation":"https://pith.science/pith/4WA5YK2COAVBW3MKOHRSTSOQPT/action/citation_signature","submit_replication":"https://pith.science/pith/4WA5YK2COAVBW3MKOHRSTSOQPT/action/replication_record"}},"created_at":"2026-07-05T07:12:43.331254+00:00","updated_at":"2026-07-05T07:12:43.331254+00:00"}