{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:MUAKJCSRLJZK5N5NCHGOXBUMAQ","short_pith_number":"pith:MUAKJCSR","schema_version":"1.0","canonical_sha256":"6500a48a515a72aeb7ad11cceb868c0434f0795b2594c40701540ab55a8722b7","source":{"kind":"arxiv","id":"2403.13335","version":1},"attestation_state":"computed","paper":{"title":"Adaptive Ensembles of Fine-Tuned Transformers for LLM-Generated Text Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Suiyao Chen, Xuesheng Zhang, Zhixin Lai","submitted_at":"2024-03-20T06:38:13Z","abstract_excerpt":"Large language models (LLMs) have reached human-like proficiency in generating diverse textual content, underscoring the necessity for effective fake text detection to avoid potential risks such as fake news in social media. Previous research has mostly tested single models on in-distribution datasets, limiting our understanding of how these models perform on different types of data for LLM-generated text detection task. We researched this by testing five specialized transformer-based models on both in-distribution and out-of-distribution datasets to better assess their performance and general"},"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":"2403.13335","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-03-20T06:38:13Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"3dae00c0397f4624bca70cfb971fff24b8b1b8962b259e6172871daa2398808a","abstract_canon_sha256":"59620384609a60f4009840f676647f5d9cbd00042fdcc5e7744c20f365a3cc95"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:58:34.915639Z","signature_b64":"weph+l6SL7GJso9UApLTYlFwORLiLSJrOgxnO+K9Y1ssXeli3hjYPiTeBOsQiL1W6J1fF6PKAsUnRhTFu0TOBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6500a48a515a72aeb7ad11cceb868c0434f0795b2594c40701540ab55a8722b7","last_reissued_at":"2026-07-05T07:58:34.915056Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:58:34.915056Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Adaptive Ensembles of Fine-Tuned Transformers for LLM-Generated Text Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Suiyao Chen, Xuesheng Zhang, Zhixin Lai","submitted_at":"2024-03-20T06:38:13Z","abstract_excerpt":"Large language models (LLMs) have reached human-like proficiency in generating diverse textual content, underscoring the necessity for effective fake text detection to avoid potential risks such as fake news in social media. Previous research has mostly tested single models on in-distribution datasets, limiting our understanding of how these models perform on different types of data for LLM-generated text detection task. We researched this by testing five specialized transformer-based models on both in-distribution and out-of-distribution datasets to better assess their performance and general"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.13335","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/2403.13335/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":"2403.13335","created_at":"2026-07-05T07:58:34.915123+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.13335v1","created_at":"2026-07-05T07:58:34.915123+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.13335","created_at":"2026-07-05T07:58:34.915123+00:00"},{"alias_kind":"pith_short_12","alias_value":"MUAKJCSRLJZK","created_at":"2026-07-05T07:58:34.915123+00:00"},{"alias_kind":"pith_short_16","alias_value":"MUAKJCSRLJZK5N5N","created_at":"2026-07-05T07:58:34.915123+00:00"},{"alias_kind":"pith_short_8","alias_value":"MUAKJCSR","created_at":"2026-07-05T07:58:34.915123+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2509.07287","citing_title":"Paladin: Defending LLM-enabled Phishing Emails with a New Trigger-Tag Paradigm","ref_index":41,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MUAKJCSRLJZK5N5NCHGOXBUMAQ","json":"https://pith.science/pith/MUAKJCSRLJZK5N5NCHGOXBUMAQ.json","graph_json":"https://pith.science/api/pith-number/MUAKJCSRLJZK5N5NCHGOXBUMAQ/graph.json","events_json":"https://pith.science/api/pith-number/MUAKJCSRLJZK5N5NCHGOXBUMAQ/events.json","paper":"https://pith.science/paper/MUAKJCSR"},"agent_actions":{"view_html":"https://pith.science/pith/MUAKJCSRLJZK5N5NCHGOXBUMAQ","download_json":"https://pith.science/pith/MUAKJCSRLJZK5N5NCHGOXBUMAQ.json","view_paper":"https://pith.science/paper/MUAKJCSR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.13335&json=true","fetch_graph":"https://pith.science/api/pith-number/MUAKJCSRLJZK5N5NCHGOXBUMAQ/graph.json","fetch_events":"https://pith.science/api/pith-number/MUAKJCSRLJZK5N5NCHGOXBUMAQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MUAKJCSRLJZK5N5NCHGOXBUMAQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MUAKJCSRLJZK5N5NCHGOXBUMAQ/action/storage_attestation","attest_author":"https://pith.science/pith/MUAKJCSRLJZK5N5NCHGOXBUMAQ/action/author_attestation","sign_citation":"https://pith.science/pith/MUAKJCSRLJZK5N5NCHGOXBUMAQ/action/citation_signature","submit_replication":"https://pith.science/pith/MUAKJCSRLJZK5N5NCHGOXBUMAQ/action/replication_record"}},"created_at":"2026-07-05T07:58:34.915123+00:00","updated_at":"2026-07-05T07:58:34.915123+00:00"}