{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:CQI4C7EV763HIMYLGPJNF4CIOX","short_pith_number":"pith:CQI4C7EV","schema_version":"1.0","canonical_sha256":"1411c17c95ffb674330b33d2d2f04875eb42728a836682472b20e148e37b2832","source":{"kind":"arxiv","id":"2407.03993","version":2},"attestation_state":"computed","paper":{"title":"A Survey on Natural Language Counterfactual Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Xiaoqi Qiu, Xu Guo, Yongjie Wang, Yuhong Feng, Yu Yue, Zhiqi Shen, Zhiwei Zeng","submitted_at":"2024-07-04T15:13:59Z","abstract_excerpt":"Natural language counterfactual generation aims to minimally modify a given text such that the modified text will be classified into a different class. The generated counterfactuals provide insight into the reasoning behind a model's predictions by highlighting which words significantly influence the outcomes. Additionally, they can be used to detect model fairness issues and augment the training data to enhance the model's robustness. A substantial amount of research has been conducted to generate counterfactuals for various NLP tasks, employing different models and methodologies. With the ra"},"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":"2407.03993","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-07-04T15:13:59Z","cross_cats_sorted":[],"title_canon_sha256":"a69f09bde146cc77398a05f0995f850a8cf29268a96154eb32f812d69cecf433","abstract_canon_sha256":"f7ded1fa7b49d179144ff32827aca7e4c0e4f8534bb8cb76107919444fe73f0c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:16:19.340422Z","signature_b64":"KrDoFMpuyUuIuLBewnGhYH2XNhioOrnMLC39XSjRRCeF/6Rm1QRGBIEqpbuu3wQHD6T4qj6Bbt8keWlQTuwrDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1411c17c95ffb674330b33d2d2f04875eb42728a836682472b20e148e37b2832","last_reissued_at":"2026-07-05T09:16:19.339938Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:16:19.339938Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Survey on Natural Language Counterfactual Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Xiaoqi Qiu, Xu Guo, Yongjie Wang, Yuhong Feng, Yu Yue, Zhiqi Shen, Zhiwei Zeng","submitted_at":"2024-07-04T15:13:59Z","abstract_excerpt":"Natural language counterfactual generation aims to minimally modify a given text such that the modified text will be classified into a different class. The generated counterfactuals provide insight into the reasoning behind a model's predictions by highlighting which words significantly influence the outcomes. Additionally, they can be used to detect model fairness issues and augment the training data to enhance the model's robustness. A substantial amount of research has been conducted to generate counterfactuals for various NLP tasks, employing different models and methodologies. With the ra"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.03993","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/2407.03993/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":"2407.03993","created_at":"2026-07-05T09:16:19.339993+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.03993v2","created_at":"2026-07-05T09:16:19.339993+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.03993","created_at":"2026-07-05T09:16:19.339993+00:00"},{"alias_kind":"pith_short_12","alias_value":"CQI4C7EV763H","created_at":"2026-07-05T09:16:19.339993+00:00"},{"alias_kind":"pith_short_16","alias_value":"CQI4C7EV763HIMYL","created_at":"2026-07-05T09:16:19.339993+00:00"},{"alias_kind":"pith_short_8","alias_value":"CQI4C7EV","created_at":"2026-07-05T09:16:19.339993+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.10220","citing_title":"How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives","ref_index":26,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CQI4C7EV763HIMYLGPJNF4CIOX","json":"https://pith.science/pith/CQI4C7EV763HIMYLGPJNF4CIOX.json","graph_json":"https://pith.science/api/pith-number/CQI4C7EV763HIMYLGPJNF4CIOX/graph.json","events_json":"https://pith.science/api/pith-number/CQI4C7EV763HIMYLGPJNF4CIOX/events.json","paper":"https://pith.science/paper/CQI4C7EV"},"agent_actions":{"view_html":"https://pith.science/pith/CQI4C7EV763HIMYLGPJNF4CIOX","download_json":"https://pith.science/pith/CQI4C7EV763HIMYLGPJNF4CIOX.json","view_paper":"https://pith.science/paper/CQI4C7EV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.03993&json=true","fetch_graph":"https://pith.science/api/pith-number/CQI4C7EV763HIMYLGPJNF4CIOX/graph.json","fetch_events":"https://pith.science/api/pith-number/CQI4C7EV763HIMYLGPJNF4CIOX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CQI4C7EV763HIMYLGPJNF4CIOX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CQI4C7EV763HIMYLGPJNF4CIOX/action/storage_attestation","attest_author":"https://pith.science/pith/CQI4C7EV763HIMYLGPJNF4CIOX/action/author_attestation","sign_citation":"https://pith.science/pith/CQI4C7EV763HIMYLGPJNF4CIOX/action/citation_signature","submit_replication":"https://pith.science/pith/CQI4C7EV763HIMYLGPJNF4CIOX/action/replication_record"}},"created_at":"2026-07-05T09:16:19.339993+00:00","updated_at":"2026-07-05T09:16:19.339993+00:00"}