{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:54MYLJ7MHNMFIRQSVBHEQC5YWY","short_pith_number":"pith:54MYLJ7M","schema_version":"1.0","canonical_sha256":"ef1985a7ec3b58544612a84e480bb8b61857150bcd0e34017a2e16093561155e","source":{"kind":"arxiv","id":"2305.02160","version":1},"attestation_state":"computed","paper":{"title":"Explaining Language Models' Predictions with High-Impact Concepts","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Ruochen Zhao, Shafiq Joty, Tan Wang, Yongjie Wang","submitted_at":"2023-05-03T14:48:27Z","abstract_excerpt":"The emergence of large-scale pretrained language models has posed unprecedented challenges in deriving explanations of why the model has made some predictions. Stemmed from the compositional nature of languages, spurious correlations have further undermined the trustworthiness of NLP systems, leading to unreliable model explanations that are merely correlated with the output predictions. To encourage fairness and transparency, there exists an urgent demand for reliable explanations that allow users to consistently understand the model's behavior. In this work, we propose a complete framework f"},"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":"2305.02160","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-05-03T14:48:27Z","cross_cats_sorted":[],"title_canon_sha256":"684d2598d04941fc0913b2838eda1b128e66065b9b7fbd3cba33e3bb73505caf","abstract_canon_sha256":"23ca8be602e49255fa7238a950defc2a72ebabba0ecb7416ee0c8442c8498ad7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:06:47.989609Z","signature_b64":"l4UVKv7fSQA6gC/oIqJliY6Fcu/fZLkBCUAWP3unCtCbATm4xsVkUnREdHRGere6a7FcUvvEjCKOkk0o6CGkAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ef1985a7ec3b58544612a84e480bb8b61857150bcd0e34017a2e16093561155e","last_reissued_at":"2026-07-05T06:06:47.989202Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:06:47.989202Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Explaining Language Models' Predictions with High-Impact Concepts","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Ruochen Zhao, Shafiq Joty, Tan Wang, Yongjie Wang","submitted_at":"2023-05-03T14:48:27Z","abstract_excerpt":"The emergence of large-scale pretrained language models has posed unprecedented challenges in deriving explanations of why the model has made some predictions. Stemmed from the compositional nature of languages, spurious correlations have further undermined the trustworthiness of NLP systems, leading to unreliable model explanations that are merely correlated with the output predictions. To encourage fairness and transparency, there exists an urgent demand for reliable explanations that allow users to consistently understand the model's behavior. In this work, we propose a complete framework f"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.02160","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/2305.02160/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":"2305.02160","created_at":"2026-07-05T06:06:47.989258+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.02160v1","created_at":"2026-07-05T06:06:47.989258+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.02160","created_at":"2026-07-05T06:06:47.989258+00:00"},{"alias_kind":"pith_short_12","alias_value":"54MYLJ7MHNMF","created_at":"2026-07-05T06:06:47.989258+00:00"},{"alias_kind":"pith_short_16","alias_value":"54MYLJ7MHNMFIRQS","created_at":"2026-07-05T06:06:47.989258+00:00"},{"alias_kind":"pith_short_8","alias_value":"54MYLJ7M","created_at":"2026-07-05T06:06:47.989258+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/54MYLJ7MHNMFIRQSVBHEQC5YWY","json":"https://pith.science/pith/54MYLJ7MHNMFIRQSVBHEQC5YWY.json","graph_json":"https://pith.science/api/pith-number/54MYLJ7MHNMFIRQSVBHEQC5YWY/graph.json","events_json":"https://pith.science/api/pith-number/54MYLJ7MHNMFIRQSVBHEQC5YWY/events.json","paper":"https://pith.science/paper/54MYLJ7M"},"agent_actions":{"view_html":"https://pith.science/pith/54MYLJ7MHNMFIRQSVBHEQC5YWY","download_json":"https://pith.science/pith/54MYLJ7MHNMFIRQSVBHEQC5YWY.json","view_paper":"https://pith.science/paper/54MYLJ7M","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.02160&json=true","fetch_graph":"https://pith.science/api/pith-number/54MYLJ7MHNMFIRQSVBHEQC5YWY/graph.json","fetch_events":"https://pith.science/api/pith-number/54MYLJ7MHNMFIRQSVBHEQC5YWY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/54MYLJ7MHNMFIRQSVBHEQC5YWY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/54MYLJ7MHNMFIRQSVBHEQC5YWY/action/storage_attestation","attest_author":"https://pith.science/pith/54MYLJ7MHNMFIRQSVBHEQC5YWY/action/author_attestation","sign_citation":"https://pith.science/pith/54MYLJ7MHNMFIRQSVBHEQC5YWY/action/citation_signature","submit_replication":"https://pith.science/pith/54MYLJ7MHNMFIRQSVBHEQC5YWY/action/replication_record"}},"created_at":"2026-07-05T06:06:47.989258+00:00","updated_at":"2026-07-05T06:06:47.989258+00:00"}