{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:EHD3A5RB354FKEJGSIHJAFCSAR","short_pith_number":"pith:EHD3A5RB","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"},"canonical_sha256":"21c7b07621df78551126920e9014520440c8a746e065c91c530ed84b8e5d36cb","source":{"kind":"arxiv","id":"2411.09947","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.09947","created_at":"2026-07-05T09:58:26Z"},{"alias_kind":"arxiv_version","alias_value":"2411.09947v2","created_at":"2026-07-05T09:58:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.09947","created_at":"2026-07-05T09:58:26Z"},{"alias_kind":"pith_short_12","alias_value":"EHD3A5RB354F","created_at":"2026-07-05T09:58:26Z"},{"alias_kind":"pith_short_16","alias_value":"EHD3A5RB354FKEJG","created_at":"2026-07-05T09:58:26Z"},{"alias_kind":"pith_short_8","alias_value":"EHD3A5RB","created_at":"2026-07-05T09:58:26Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:EHD3A5RB354FKEJGSIHJAFCSAR","target":"record","payload":{"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"},"canonical_sha256":"21c7b07621df78551126920e9014520440c8a746e065c91c530ed84b8e5d36cb","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"},"source_kind":"arxiv","source_id":"2411.09947","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:58:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"i5LA4mfGA2ZhVqLkXdthw91IDZ21swHyw7ussxg2HE7QK0B5TBsnr0F8htZHvNdtBPtBZoSdM1/WsK/mK7vyBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T08:14:57.521067Z"},"content_sha256":"906360b07e51f872ddc55bf6ee9f6e81a5917a3cf1c7b2cc0cf9a0f2ff82985c","schema_version":"1.0","event_id":"sha256:906360b07e51f872ddc55bf6ee9f6e81a5917a3cf1c7b2cc0cf9a0f2ff82985c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:EHD3A5RB354FKEJGSIHJAFCSAR","target":"graph","payload":{"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:58:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UhtVC9WzrQv7cxUq+7b9eOgsn6nhEwEfWeASdrp4u2MwbfdAbR6Gf2SMrCzB7KLYM+b9V0cG4XYKlKWMeCjFCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T08:14:57.521456Z"},"content_sha256":"d647a877b1589d35b6b83b43cc6e925e11e54d46b60920000f781cee594d5308","schema_version":"1.0","event_id":"sha256:d647a877b1589d35b6b83b43cc6e925e11e54d46b60920000f781cee594d5308"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EHD3A5RB354FKEJGSIHJAFCSAR/bundle.json","state_url":"https://pith.science/pith/EHD3A5RB354FKEJGSIHJAFCSAR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EHD3A5RB354FKEJGSIHJAFCSAR/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-13T08:14:57Z","links":{"resolver":"https://pith.science/pith/EHD3A5RB354FKEJGSIHJAFCSAR","bundle":"https://pith.science/pith/EHD3A5RB354FKEJGSIHJAFCSAR/bundle.json","state":"https://pith.science/pith/EHD3A5RB354FKEJGSIHJAFCSAR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EHD3A5RB354FKEJGSIHJAFCSAR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:EHD3A5RB354FKEJGSIHJAFCSAR","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"c165adea7e40da8e2775180896f8a7fa2ba763822baabf512d3864c12b2bbb00","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-11-15T04:57:13Z","title_canon_sha256":"34a78712cd9353ce63cdb87ad0d9af5b7e4f3d05aeecb8fa14f20a2e304ff619"},"schema_version":"1.0","source":{"id":"2411.09947","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.09947","created_at":"2026-07-05T09:58:26Z"},{"alias_kind":"arxiv_version","alias_value":"2411.09947v2","created_at":"2026-07-05T09:58:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.09947","created_at":"2026-07-05T09:58:26Z"},{"alias_kind":"pith_short_12","alias_value":"EHD3A5RB354F","created_at":"2026-07-05T09:58:26Z"},{"alias_kind":"pith_short_16","alias_value":"EHD3A5RB354FKEJG","created_at":"2026-07-05T09:58:26Z"},{"alias_kind":"pith_short_8","alias_value":"EHD3A5RB","created_at":"2026-07-05T09:58:26Z"}],"graph_snapshots":[{"event_id":"sha256:d647a877b1589d35b6b83b43cc6e925e11e54d46b60920000f781cee594d5308","target":"graph","created_at":"2026-07-05T09:58:26Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2411.09947/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"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","authors_text":"Chunliang Tao, Xiaojing Fan, Yahe Yang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-11-15T04:57:13Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.09947","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:906360b07e51f872ddc55bf6ee9f6e81a5917a3cf1c7b2cc0cf9a0f2ff82985c","target":"record","created_at":"2026-07-05T09:58:26Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"c165adea7e40da8e2775180896f8a7fa2ba763822baabf512d3864c12b2bbb00","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-11-15T04:57:13Z","title_canon_sha256":"34a78712cd9353ce63cdb87ad0d9af5b7e4f3d05aeecb8fa14f20a2e304ff619"},"schema_version":"1.0","source":{"id":"2411.09947","kind":"arxiv","version":2}},"canonical_sha256":"21c7b07621df78551126920e9014520440c8a746e065c91c530ed84b8e5d36cb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"21c7b07621df78551126920e9014520440c8a746e065c91c530ed84b8e5d36cb","first_computed_at":"2026-07-05T09:58:26.157273Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:58:26.157273Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"1RJDa9emzKtuc6IbLQMGzhVtnX8lBF7qBDdu4Oe6uNEW6G7aajK5pVnDRCJD/7ncYhHDL0/HDnEz/qxREivVCg==","signature_status":"signed_v1","signed_at":"2026-07-05T09:58:26.157788Z","signed_message":"canonical_sha256_bytes"},"source_id":"2411.09947","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:906360b07e51f872ddc55bf6ee9f6e81a5917a3cf1c7b2cc0cf9a0f2ff82985c","sha256:d647a877b1589d35b6b83b43cc6e925e11e54d46b60920000f781cee594d5308"],"state_sha256":"3ea4abd90a25f659454d5fc1d5992ac4e19bfc5bca26376e39254de902b57c90"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mJq2sYaUVxK6xG0kh/mCVIG01effTT3w+t++fBiThpB8rmhh5rXSKt68vxmstFAijh/2V2IUs2+PVk9MgYB5CA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-13T08:14:57.523860Z","bundle_sha256":"70bb5588b1841f118b847be4739b3e74ae2bd73c651f85608cdfd83777bee15f"}}