{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:MLSLBG7WDB6XIMMOVOP2L7PW4Z","short_pith_number":"pith:MLSLBG7W","schema_version":"1.0","canonical_sha256":"62e4b09bf6187d74318eab9fa5fdf6e668d777ef7fed490438a9621cbd9861eb","source":{"kind":"arxiv","id":"2411.12764","version":1},"attestation_state":"computed","paper":{"title":"SEFD: Semantic-Enhanced Framework for Detecting LLM-Generated Text","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.IR"],"primary_cat":"cs.CL","authors_text":"Bojian Hou, Davoud Ataee Tarzanagh, Li Shen, Qi Long, Tianqi Shang, Weiqing He","submitted_at":"2024-11-17T20:13:30Z","abstract_excerpt":"The widespread adoption of large language models (LLMs) has created an urgent need for robust tools to detect LLM-generated text, especially in light of \\textit{paraphrasing} techniques that often evade existing detection methods. To address this challenge, we present a novel semantic-enhanced framework for detecting LLM-generated text (SEFD) that leverages a retrieval-based mechanism to fully utilize text semantics. Our framework improves upon existing detection methods by systematically integrating retrieval-based techniques with traditional detectors, employing a carefully curated retrieval"},"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":"2411.12764","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-11-17T20:13:30Z","cross_cats_sorted":["cs.AI","cs.IR"],"title_canon_sha256":"3a556a9c4e62d197f6dc2bcd3d76155cac1d67f845b3a449c5741b69ebb51a7c","abstract_canon_sha256":"1d0b092f1c24409f1b957a08c5318f07ff673a6b4b825a7e9b0ba52656209002"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:38:16.113341Z","signature_b64":"eT7+Uexu3ZDQ0T/Ft28nTKuHd7wiS4K+cwCe6+pNSA3tOAcFHgN6tMUyAZNHTJlWWn0lyadLlMv94Ij+9BoBAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"62e4b09bf6187d74318eab9fa5fdf6e668d777ef7fed490438a9621cbd9861eb","last_reissued_at":"2026-07-05T09:38:16.112938Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:38:16.112938Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SEFD: Semantic-Enhanced Framework for Detecting LLM-Generated Text","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.IR"],"primary_cat":"cs.CL","authors_text":"Bojian Hou, Davoud Ataee Tarzanagh, Li Shen, Qi Long, Tianqi Shang, Weiqing He","submitted_at":"2024-11-17T20:13:30Z","abstract_excerpt":"The widespread adoption of large language models (LLMs) has created an urgent need for robust tools to detect LLM-generated text, especially in light of \\textit{paraphrasing} techniques that often evade existing detection methods. To address this challenge, we present a novel semantic-enhanced framework for detecting LLM-generated text (SEFD) that leverages a retrieval-based mechanism to fully utilize text semantics. Our framework improves upon existing detection methods by systematically integrating retrieval-based techniques with traditional detectors, employing a carefully curated retrieval"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.12764","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/2411.12764/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":"2411.12764","created_at":"2026-07-05T09:38:16.112993+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.12764v1","created_at":"2026-07-05T09:38:16.112993+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.12764","created_at":"2026-07-05T09:38:16.112993+00:00"},{"alias_kind":"pith_short_12","alias_value":"MLSLBG7WDB6X","created_at":"2026-07-05T09:38:16.112993+00:00"},{"alias_kind":"pith_short_16","alias_value":"MLSLBG7WDB6XIMMO","created_at":"2026-07-05T09:38:16.112993+00:00"},{"alias_kind":"pith_short_8","alias_value":"MLSLBG7W","created_at":"2026-07-05T09:38:16.112993+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/MLSLBG7WDB6XIMMOVOP2L7PW4Z","json":"https://pith.science/pith/MLSLBG7WDB6XIMMOVOP2L7PW4Z.json","graph_json":"https://pith.science/api/pith-number/MLSLBG7WDB6XIMMOVOP2L7PW4Z/graph.json","events_json":"https://pith.science/api/pith-number/MLSLBG7WDB6XIMMOVOP2L7PW4Z/events.json","paper":"https://pith.science/paper/MLSLBG7W"},"agent_actions":{"view_html":"https://pith.science/pith/MLSLBG7WDB6XIMMOVOP2L7PW4Z","download_json":"https://pith.science/pith/MLSLBG7WDB6XIMMOVOP2L7PW4Z.json","view_paper":"https://pith.science/paper/MLSLBG7W","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.12764&json=true","fetch_graph":"https://pith.science/api/pith-number/MLSLBG7WDB6XIMMOVOP2L7PW4Z/graph.json","fetch_events":"https://pith.science/api/pith-number/MLSLBG7WDB6XIMMOVOP2L7PW4Z/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MLSLBG7WDB6XIMMOVOP2L7PW4Z/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MLSLBG7WDB6XIMMOVOP2L7PW4Z/action/storage_attestation","attest_author":"https://pith.science/pith/MLSLBG7WDB6XIMMOVOP2L7PW4Z/action/author_attestation","sign_citation":"https://pith.science/pith/MLSLBG7WDB6XIMMOVOP2L7PW4Z/action/citation_signature","submit_replication":"https://pith.science/pith/MLSLBG7WDB6XIMMOVOP2L7PW4Z/action/replication_record"}},"created_at":"2026-07-05T09:38:16.112993+00:00","updated_at":"2026-07-05T09:38:16.112993+00:00"}