{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:EHD3A5RB354FKEJGSIHJAFCSAR","short_pith_number":"pith:EHD3A5RB","schema_version":"1.0","canonical_sha256":"21c7b07621df78551126920e9014520440c8a746e065c91c530ed84b8e5d36cb","source":{"kind":"arxiv","id":"2411.09947","version":2},"attestation_state":"computed","paper":{"title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Chunliang Tao, Xiaojing Fan, Yahe Yang","submitted_at":"2024-11-15T04:57:13Z","abstract_excerpt":"Effective preference tuning is pivotal in aligning chatbot responses with human expectations, enhancing user satisfaction and engagement. Traditional approaches, notably Reinforcement Learning from Human Feedback (RLHF) as employed in advanced models like GPT-4, have demonstrated considerable success in this domain. However, RLHF methods are often computationally intensive and resource-demanding, limiting their scalability and accessibility for broader applications. To address these challenges, this study introduces LoRA-Lite Ensemble (LoRA-LiteE), an innovative framework that combines Supervi"},"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":"2411.09947","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-11-15T04:57:13Z","cross_cats_sorted":[],"title_canon_sha256":"34a78712cd9353ce63cdb87ad0d9af5b7e4f3d05aeecb8fa14f20a2e304ff619","abstract_canon_sha256":"c165adea7e40da8e2775180896f8a7fa2ba763822baabf512d3864c12b2bbb00"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:58:26.157788Z","signature_b64":"1RJDa9emzKtuc6IbLQMGzhVtnX8lBF7qBDdu4Oe6uNEW6G7aajK5pVnDRCJD/7ncYhHDL0/HDnEz/qxREivVCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"21c7b07621df78551126920e9014520440c8a746e065c91c530ed84b8e5d36cb","last_reissued_at":"2026-07-05T09:58:26.157273Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:58:26.157273Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Chunliang Tao, Xiaojing Fan, Yahe Yang","submitted_at":"2024-11-15T04:57:13Z","abstract_excerpt":"Effective preference tuning is pivotal in aligning chatbot responses with human expectations, enhancing user satisfaction and engagement. Traditional approaches, notably Reinforcement Learning from Human Feedback (RLHF) as employed in advanced models like GPT-4, have demonstrated considerable success in this domain. However, RLHF methods are often computationally intensive and resource-demanding, limiting their scalability and accessibility for broader applications. To address these challenges, this study introduces LoRA-Lite Ensemble (LoRA-LiteE), an innovative framework that combines Supervi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.09947","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/2411.09947/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":"2411.09947","created_at":"2026-07-05T09:58:26.157339+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.09947v2","created_at":"2026-07-05T09:58:26.157339+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.09947","created_at":"2026-07-05T09:58:26.157339+00:00"},{"alias_kind":"pith_short_12","alias_value":"EHD3A5RB354F","created_at":"2026-07-05T09:58:26.157339+00:00"},{"alias_kind":"pith_short_16","alias_value":"EHD3A5RB354FKEJG","created_at":"2026-07-05T09:58:26.157339+00:00"},{"alias_kind":"pith_short_8","alias_value":"EHD3A5RB","created_at":"2026-07-05T09:58:26.157339+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.07173","citing_title":"InfiniLoRA: Disaggregated Multi-LoRA Serving for Large Language Models","ref_index":50,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EHD3A5RB354FKEJGSIHJAFCSAR","json":"https://pith.science/pith/EHD3A5RB354FKEJGSIHJAFCSAR.json","graph_json":"https://pith.science/api/pith-number/EHD3A5RB354FKEJGSIHJAFCSAR/graph.json","events_json":"https://pith.science/api/pith-number/EHD3A5RB354FKEJGSIHJAFCSAR/events.json","paper":"https://pith.science/paper/EHD3A5RB"},"agent_actions":{"view_html":"https://pith.science/pith/EHD3A5RB354FKEJGSIHJAFCSAR","download_json":"https://pith.science/pith/EHD3A5RB354FKEJGSIHJAFCSAR.json","view_paper":"https://pith.science/paper/EHD3A5RB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.09947&json=true","fetch_graph":"https://pith.science/api/pith-number/EHD3A5RB354FKEJGSIHJAFCSAR/graph.json","fetch_events":"https://pith.science/api/pith-number/EHD3A5RB354FKEJGSIHJAFCSAR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EHD3A5RB354FKEJGSIHJAFCSAR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EHD3A5RB354FKEJGSIHJAFCSAR/action/storage_attestation","attest_author":"https://pith.science/pith/EHD3A5RB354FKEJGSIHJAFCSAR/action/author_attestation","sign_citation":"https://pith.science/pith/EHD3A5RB354FKEJGSIHJAFCSAR/action/citation_signature","submit_replication":"https://pith.science/pith/EHD3A5RB354FKEJGSIHJAFCSAR/action/replication_record"}},"created_at":"2026-07-05T09:58:26.157339+00:00","updated_at":"2026-07-05T09:58:26.157339+00:00"}