{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:HNLIATGGPCVWVX2JXVAINJBXKF","short_pith_number":"pith:HNLIATGG","schema_version":"1.0","canonical_sha256":"3b56804cc678ab6adf49bd4086a4375146f50012416a625701f379f8c7485a41","source":{"kind":"arxiv","id":"2508.16969","version":1},"attestation_state":"computed","paper":{"title":"Explaining Black-box Language Models with Knowledge Probing Systems: A Post-hoc Explanation Perspective","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.DB"],"primary_cat":"cs.CL","authors_text":"Hao Xu, Jiye Liang, Ru Li, Xiaoli Li, Yunxiao Zhao, Zhiqiang Wang","submitted_at":"2025-08-23T09:41:59Z","abstract_excerpt":"Pre-trained Language Models (PLMs) are trained on large amounts of unlabeled data, yet they exhibit remarkable reasoning skills. However, the trustworthiness challenges posed by these black-box models have become increasingly evident in recent years. To alleviate this problem, this paper proposes a novel Knowledge-guided Probing approach called KnowProb in a post-hoc explanation way, which aims to probe whether black-box PLMs understand implicit knowledge beyond the given text, rather than focusing only on the surface level content of the text. We provide six potential explanations derived fro"},"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":"2508.16969","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-08-23T09:41:59Z","cross_cats_sorted":["cs.AI","cs.DB"],"title_canon_sha256":"fcf1d0c34a5ce3ebb3b31a11c059762f450de9ca550056d2112cfaabfbffb0e6","abstract_canon_sha256":"78753d5bd5748628ac2195b2c470d65603b0f03bc906968cec058ab8e91e2811"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:58:07.436123Z","signature_b64":"e8sIlJy+SAqTeaOIWJjS7ghIIodUM/D4MSygEDIJqx/+xbsFOj0f7UlHWu15sRDvqAcA9Yo1nv8YER75+5UDDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3b56804cc678ab6adf49bd4086a4375146f50012416a625701f379f8c7485a41","last_reissued_at":"2026-07-05T11:58:07.435711Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:58:07.435711Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Explaining Black-box Language Models with Knowledge Probing Systems: A Post-hoc Explanation Perspective","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.DB"],"primary_cat":"cs.CL","authors_text":"Hao Xu, Jiye Liang, Ru Li, Xiaoli Li, Yunxiao Zhao, Zhiqiang Wang","submitted_at":"2025-08-23T09:41:59Z","abstract_excerpt":"Pre-trained Language Models (PLMs) are trained on large amounts of unlabeled data, yet they exhibit remarkable reasoning skills. However, the trustworthiness challenges posed by these black-box models have become increasingly evident in recent years. To alleviate this problem, this paper proposes a novel Knowledge-guided Probing approach called KnowProb in a post-hoc explanation way, which aims to probe whether black-box PLMs understand implicit knowledge beyond the given text, rather than focusing only on the surface level content of the text. We provide six potential explanations derived fro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.16969","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/2508.16969/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":"2508.16969","created_at":"2026-07-05T11:58:07.435774+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.16969v1","created_at":"2026-07-05T11:58:07.435774+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.16969","created_at":"2026-07-05T11:58:07.435774+00:00"},{"alias_kind":"pith_short_12","alias_value":"HNLIATGGPCVW","created_at":"2026-07-05T11:58:07.435774+00:00"},{"alias_kind":"pith_short_16","alias_value":"HNLIATGGPCVWVX2J","created_at":"2026-07-05T11:58:07.435774+00:00"},{"alias_kind":"pith_short_8","alias_value":"HNLIATGG","created_at":"2026-07-05T11:58:07.435774+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2509.04866","citing_title":"Memorization $\\neq$ Understanding: Do Large Language Models Have the Ability of Scenario Cognition?","ref_index":39,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HNLIATGGPCVWVX2JXVAINJBXKF","json":"https://pith.science/pith/HNLIATGGPCVWVX2JXVAINJBXKF.json","graph_json":"https://pith.science/api/pith-number/HNLIATGGPCVWVX2JXVAINJBXKF/graph.json","events_json":"https://pith.science/api/pith-number/HNLIATGGPCVWVX2JXVAINJBXKF/events.json","paper":"https://pith.science/paper/HNLIATGG"},"agent_actions":{"view_html":"https://pith.science/pith/HNLIATGGPCVWVX2JXVAINJBXKF","download_json":"https://pith.science/pith/HNLIATGGPCVWVX2JXVAINJBXKF.json","view_paper":"https://pith.science/paper/HNLIATGG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.16969&json=true","fetch_graph":"https://pith.science/api/pith-number/HNLIATGGPCVWVX2JXVAINJBXKF/graph.json","fetch_events":"https://pith.science/api/pith-number/HNLIATGGPCVWVX2JXVAINJBXKF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HNLIATGGPCVWVX2JXVAINJBXKF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HNLIATGGPCVWVX2JXVAINJBXKF/action/storage_attestation","attest_author":"https://pith.science/pith/HNLIATGGPCVWVX2JXVAINJBXKF/action/author_attestation","sign_citation":"https://pith.science/pith/HNLIATGGPCVWVX2JXVAINJBXKF/action/citation_signature","submit_replication":"https://pith.science/pith/HNLIATGGPCVWVX2JXVAINJBXKF/action/replication_record"}},"created_at":"2026-07-05T11:58:07.435774+00:00","updated_at":"2026-07-05T11:58:07.435774+00:00"}