{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:WD6VZCTCNQW3Y7DEC74PTJC4AQ","short_pith_number":"pith:WD6VZCTC","schema_version":"1.0","canonical_sha256":"b0fd5c8a626c2dbc7c6417f8f9a45c0421069f01a32476ace12d01aea62ed18b","source":{"kind":"arxiv","id":"2401.08046","version":1},"attestation_state":"computed","paper":{"title":"Enhancing Robustness of LLM-Synthetic Text Detectors for Academic Writing: A Comprehensive Analysis","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Ching-Chun Chang, Huy H. Nguyen, Isao Echizen, Yuchen Guo, Zhicheng Dou","submitted_at":"2024-01-16T01:58:36Z","abstract_excerpt":"The emergence of large language models (LLMs), such as Generative Pre-trained Transformer 4 (GPT-4) used by ChatGPT, has profoundly impacted the academic and broader community. While these models offer numerous advantages in terms of revolutionizing work and study methods, they have also garnered significant attention due to their potential negative consequences. One example is generating academic reports or papers with little to no human contribution. Consequently, researchers have focused on developing detectors to address the misuse of LLMs. However, most existing methods prioritize achievi"},"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":"2401.08046","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-01-16T01:58:36Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"355ca857574c3a82ab0e732ff5aa44e1cf598e1185dd151249f13ad0bfe1c81e","abstract_canon_sha256":"d0724e7c42ed54a96186fd52968987a57fd687bac7592f6d8d374a842b894a03"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:34:06.506681Z","signature_b64":"gimLHC61fVHxMm/4uTWJgBX4rleyWbtVArTqDrVDVXOPJPhHnUfFigCbEpule2S5Rsm2wDxqQXAkhvgcZ4PDDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b0fd5c8a626c2dbc7c6417f8f9a45c0421069f01a32476ace12d01aea62ed18b","last_reissued_at":"2026-07-05T07:34:06.506178Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:34:06.506178Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Enhancing Robustness of LLM-Synthetic Text Detectors for Academic Writing: A Comprehensive Analysis","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Ching-Chun Chang, Huy H. Nguyen, Isao Echizen, Yuchen Guo, Zhicheng Dou","submitted_at":"2024-01-16T01:58:36Z","abstract_excerpt":"The emergence of large language models (LLMs), such as Generative Pre-trained Transformer 4 (GPT-4) used by ChatGPT, has profoundly impacted the academic and broader community. While these models offer numerous advantages in terms of revolutionizing work and study methods, they have also garnered significant attention due to their potential negative consequences. One example is generating academic reports or papers with little to no human contribution. Consequently, researchers have focused on developing detectors to address the misuse of LLMs. However, most existing methods prioritize achievi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.08046","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/2401.08046/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":"2401.08046","created_at":"2026-07-05T07:34:06.506237+00:00"},{"alias_kind":"arxiv_version","alias_value":"2401.08046v1","created_at":"2026-07-05T07:34:06.506237+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.08046","created_at":"2026-07-05T07:34:06.506237+00:00"},{"alias_kind":"pith_short_12","alias_value":"WD6VZCTCNQW3","created_at":"2026-07-05T07:34:06.506237+00:00"},{"alias_kind":"pith_short_16","alias_value":"WD6VZCTCNQW3Y7DE","created_at":"2026-07-05T07:34:06.506237+00:00"},{"alias_kind":"pith_short_8","alias_value":"WD6VZCTC","created_at":"2026-07-05T07:34:06.506237+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/WD6VZCTCNQW3Y7DEC74PTJC4AQ","json":"https://pith.science/pith/WD6VZCTCNQW3Y7DEC74PTJC4AQ.json","graph_json":"https://pith.science/api/pith-number/WD6VZCTCNQW3Y7DEC74PTJC4AQ/graph.json","events_json":"https://pith.science/api/pith-number/WD6VZCTCNQW3Y7DEC74PTJC4AQ/events.json","paper":"https://pith.science/paper/WD6VZCTC"},"agent_actions":{"view_html":"https://pith.science/pith/WD6VZCTCNQW3Y7DEC74PTJC4AQ","download_json":"https://pith.science/pith/WD6VZCTCNQW3Y7DEC74PTJC4AQ.json","view_paper":"https://pith.science/paper/WD6VZCTC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2401.08046&json=true","fetch_graph":"https://pith.science/api/pith-number/WD6VZCTCNQW3Y7DEC74PTJC4AQ/graph.json","fetch_events":"https://pith.science/api/pith-number/WD6VZCTCNQW3Y7DEC74PTJC4AQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WD6VZCTCNQW3Y7DEC74PTJC4AQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WD6VZCTCNQW3Y7DEC74PTJC4AQ/action/storage_attestation","attest_author":"https://pith.science/pith/WD6VZCTCNQW3Y7DEC74PTJC4AQ/action/author_attestation","sign_citation":"https://pith.science/pith/WD6VZCTCNQW3Y7DEC74PTJC4AQ/action/citation_signature","submit_replication":"https://pith.science/pith/WD6VZCTCNQW3Y7DEC74PTJC4AQ/action/replication_record"}},"created_at":"2026-07-05T07:34:06.506237+00:00","updated_at":"2026-07-05T07:34:06.506237+00:00"}