{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:GXFUHCF5XUNZQJ2XGMHMLJNTKH","short_pith_number":"pith:GXFUHCF5","schema_version":"1.0","canonical_sha256":"35cb4388bdbd1b982757330ec5a5b351e9cebd48651bf0590b7ab9f01824185a","source":{"kind":"arxiv","id":"2309.16289","version":1},"attestation_state":"computed","paper":{"title":"LawBench: Benchmarking Legal Knowledge of Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Dawei Zhu, Fengzhe Zhou, Jidong Ge, Kai Chen, Songyang Zhang, Xiaoyu Shen, Zhiwei Fei, Zhuo Han, Zongwen Shen","submitted_at":"2023-09-28T09:35:59Z","abstract_excerpt":"Large language models (LLMs) have demonstrated strong capabilities in various aspects. However, when applying them to the highly specialized, safe-critical legal domain, it is unclear how much legal knowledge they possess and whether they can reliably perform legal-related tasks. To address this gap, we propose a comprehensive evaluation benchmark LawBench. LawBench has been meticulously crafted to have precise assessment of the LLMs' legal capabilities from three cognitive levels: (1) Legal knowledge memorization: whether LLMs can memorize needed legal concepts, articles and facts; (2) Legal "},"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":"2309.16289","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-09-28T09:35:59Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"3ce73524ef28ffa3ccac776b6e34f7d321882ce80cecfcf9ee59b530067bba3c","abstract_canon_sha256":"8cd2fb445f8fb0f4fed85201ac3e512ec521fdd501f916040752c2493027315d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:55:14.111215Z","signature_b64":"LgaFWE2TU31Pa8Nl8yRLl2UCrD0S3SFicXnf0puHBDBBgQvOrklgenZDJL9LHJ9WzA9nOzFlFhd156OoY2UGDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"35cb4388bdbd1b982757330ec5a5b351e9cebd48651bf0590b7ab9f01824185a","last_reissued_at":"2026-07-05T06:55:14.110783Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:55:14.110783Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LawBench: Benchmarking Legal Knowledge of Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Dawei Zhu, Fengzhe Zhou, Jidong Ge, Kai Chen, Songyang Zhang, Xiaoyu Shen, Zhiwei Fei, Zhuo Han, Zongwen Shen","submitted_at":"2023-09-28T09:35:59Z","abstract_excerpt":"Large language models (LLMs) have demonstrated strong capabilities in various aspects. However, when applying them to the highly specialized, safe-critical legal domain, it is unclear how much legal knowledge they possess and whether they can reliably perform legal-related tasks. To address this gap, we propose a comprehensive evaluation benchmark LawBench. LawBench has been meticulously crafted to have precise assessment of the LLMs' legal capabilities from three cognitive levels: (1) Legal knowledge memorization: whether LLMs can memorize needed legal concepts, articles and facts; (2) Legal "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.16289","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/2309.16289/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":"2309.16289","created_at":"2026-07-05T06:55:14.110835+00:00"},{"alias_kind":"arxiv_version","alias_value":"2309.16289v1","created_at":"2026-07-05T06:55:14.110835+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.16289","created_at":"2026-07-05T06:55:14.110835+00:00"},{"alias_kind":"pith_short_12","alias_value":"GXFUHCF5XUNZ","created_at":"2026-07-05T06:55:14.110835+00:00"},{"alias_kind":"pith_short_16","alias_value":"GXFUHCF5XUNZQJ2X","created_at":"2026-07-05T06:55:14.110835+00:00"},{"alias_kind":"pith_short_8","alias_value":"GXFUHCF5","created_at":"2026-07-05T06:55:14.110835+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":7,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.24585","citing_title":"LLMs Prompted for Legal Context Object More: Overrefusal from Small On-Premises LLMs in Criminal Legal Context","ref_index":4,"is_internal_anchor":false},{"citing_arxiv_id":"2605.13779","citing_title":"MinT: Managed Infrastructure for Training and Serving Millions of LLMs","ref_index":5,"is_internal_anchor":false},{"citing_arxiv_id":"2605.22079","citing_title":"Ishigaki-IDS-Bench: A Benchmark for Generating Information Delivery Specification from BIM Information Requirements","ref_index":7,"is_internal_anchor":false},{"citing_arxiv_id":"2605.22079","citing_title":"Ishigaki-IDS-Bench: A Benchmark for Generating Information Delivery Specification from BIM Information Requirements","ref_index":7,"is_internal_anchor":false},{"citing_arxiv_id":"2401.05561","citing_title":"TrustLLM: Trustworthiness in Large Language Models","ref_index":120,"is_internal_anchor":false},{"citing_arxiv_id":"2605.13779","citing_title":"MinT: Managed Infrastructure for Training and Serving Millions of LLMs","ref_index":5,"is_internal_anchor":false},{"citing_arxiv_id":"2402.17177","citing_title":"Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models","ref_index":142,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GXFUHCF5XUNZQJ2XGMHMLJNTKH","json":"https://pith.science/pith/GXFUHCF5XUNZQJ2XGMHMLJNTKH.json","graph_json":"https://pith.science/api/pith-number/GXFUHCF5XUNZQJ2XGMHMLJNTKH/graph.json","events_json":"https://pith.science/api/pith-number/GXFUHCF5XUNZQJ2XGMHMLJNTKH/events.json","paper":"https://pith.science/paper/GXFUHCF5"},"agent_actions":{"view_html":"https://pith.science/pith/GXFUHCF5XUNZQJ2XGMHMLJNTKH","download_json":"https://pith.science/pith/GXFUHCF5XUNZQJ2XGMHMLJNTKH.json","view_paper":"https://pith.science/paper/GXFUHCF5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2309.16289&json=true","fetch_graph":"https://pith.science/api/pith-number/GXFUHCF5XUNZQJ2XGMHMLJNTKH/graph.json","fetch_events":"https://pith.science/api/pith-number/GXFUHCF5XUNZQJ2XGMHMLJNTKH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GXFUHCF5XUNZQJ2XGMHMLJNTKH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GXFUHCF5XUNZQJ2XGMHMLJNTKH/action/storage_attestation","attest_author":"https://pith.science/pith/GXFUHCF5XUNZQJ2XGMHMLJNTKH/action/author_attestation","sign_citation":"https://pith.science/pith/GXFUHCF5XUNZQJ2XGMHMLJNTKH/action/citation_signature","submit_replication":"https://pith.science/pith/GXFUHCF5XUNZQJ2XGMHMLJNTKH/action/replication_record"}},"created_at":"2026-07-05T06:55:14.110835+00:00","updated_at":"2026-07-05T06:55:14.110835+00:00"}