{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:WU3BLV3I2FUPGPFZMRQJSLG3EH","short_pith_number":"pith:WU3BLV3I","canonical_record":{"source":{"id":"2405.17337","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-05-27T16:37:34Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"6e2b7bb29002bcb5cbec5b9af7b0fc46d8566a455cd5ef57a1ab00896e964761","abstract_canon_sha256":"a4d908d7603ecece9bb9bd3d1c3145bd7ebe33d5bbd220ea04f59c162022c5b8"},"schema_version":"1.0"},"canonical_sha256":"b53615d768d168f33cb96460992cdb21d35e0e1a6e4a38bbacc8ea540c832cf6","source":{"kind":"arxiv","id":"2405.17337","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.17337","created_at":"2026-07-05T08:23:43Z"},{"alias_kind":"arxiv_version","alias_value":"2405.17337v1","created_at":"2026-07-05T08:23:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.17337","created_at":"2026-07-05T08:23:43Z"},{"alias_kind":"pith_short_12","alias_value":"WU3BLV3I2FUP","created_at":"2026-07-05T08:23:43Z"},{"alias_kind":"pith_short_16","alias_value":"WU3BLV3I2FUPGPFZ","created_at":"2026-07-05T08:23:43Z"},{"alias_kind":"pith_short_8","alias_value":"WU3BLV3I","created_at":"2026-07-05T08:23:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:WU3BLV3I2FUPGPFZMRQJSLG3EH","target":"record","payload":{"canonical_record":{"source":{"id":"2405.17337","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-05-27T16:37:34Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"6e2b7bb29002bcb5cbec5b9af7b0fc46d8566a455cd5ef57a1ab00896e964761","abstract_canon_sha256":"a4d908d7603ecece9bb9bd3d1c3145bd7ebe33d5bbd220ea04f59c162022c5b8"},"schema_version":"1.0"},"canonical_sha256":"b53615d768d168f33cb96460992cdb21d35e0e1a6e4a38bbacc8ea540c832cf6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:23:43.066474Z","signature_b64":"b6PyYhpAzROLC5+GHi5ANicH7NLOvdTCZ8LDhHDjanTtf90hRw3gilkhKcNxrFngosTlQADf42M6qnrqfw6oAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b53615d768d168f33cb96460992cdb21d35e0e1a6e4a38bbacc8ea540c832cf6","last_reissued_at":"2026-07-05T08:23:43.065970Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:23:43.065970Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2405.17337","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:23:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QKeLL2Ka1vbRZaEuHSKEoGwnQawysqSS7gEfm8jBE8DH30619P+8zkqoX3VjZYtKYSvnYU6BQIBhBuBlcypVAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T07:58:41.710902Z"},"content_sha256":"8119623a4b019523fd00dedff08fd8e9d8a8c910fb1cc757fb852c181b1feff7","schema_version":"1.0","event_id":"sha256:8119623a4b019523fd00dedff08fd8e9d8a8c910fb1cc757fb852c181b1feff7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:WU3BLV3I2FUPGPFZMRQJSLG3EH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Cost-efficient Knowledge-based Question Answering with Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Chuang Zhou, Daochen Zha, Hao Chen, Junnan Dong, Qinggang Zhang, Xiao Huang","submitted_at":"2024-05-27T16:37:34Z","abstract_excerpt":"Knowledge-based question answering (KBQA) is widely used in many scenarios that necessitate domain knowledge. Large language models (LLMs) bring opportunities to KBQA, while their costs are significantly higher and absence of domain-specific knowledge during pre-training. We are motivated to combine LLMs and prior small models on knowledge graphs (KGMs) for both inferential accuracy and cost saving. However, it remains challenging since accuracy and cost are not readily combined in the optimization as two distinct metrics. It is also laborious for model selection since different models excel i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.17337","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/2405.17337/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":"2026-07-05T08:23:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vBRpzMTJIAr1h3I1qvYz4KJczCe8dLnmDu+G23ktd42Upui5Rhw4bB44XTiqBYD9dZwpdrBhC/PEa0vG5HFbCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T07:58:41.711745Z"},"content_sha256":"135dbb9dcc1070288d23b72d4a988f26a2d67d43e66ed9456bf84e9a6f0e0360","schema_version":"1.0","event_id":"sha256:135dbb9dcc1070288d23b72d4a988f26a2d67d43e66ed9456bf84e9a6f0e0360"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WU3BLV3I2FUPGPFZMRQJSLG3EH/bundle.json","state_url":"https://pith.science/pith/WU3BLV3I2FUPGPFZMRQJSLG3EH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WU3BLV3I2FUPGPFZMRQJSLG3EH/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":"2026-08-09T07:58:41Z","links":{"resolver":"https://pith.science/pith/WU3BLV3I2FUPGPFZMRQJSLG3EH","bundle":"https://pith.science/pith/WU3BLV3I2FUPGPFZMRQJSLG3EH/bundle.json","state":"https://pith.science/pith/WU3BLV3I2FUPGPFZMRQJSLG3EH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WU3BLV3I2FUPGPFZMRQJSLG3EH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:WU3BLV3I2FUPGPFZMRQJSLG3EH","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":"a4d908d7603ecece9bb9bd3d1c3145bd7ebe33d5bbd220ea04f59c162022c5b8","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-05-27T16:37:34Z","title_canon_sha256":"6e2b7bb29002bcb5cbec5b9af7b0fc46d8566a455cd5ef57a1ab00896e964761"},"schema_version":"1.0","source":{"id":"2405.17337","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.17337","created_at":"2026-07-05T08:23:43Z"},{"alias_kind":"arxiv_version","alias_value":"2405.17337v1","created_at":"2026-07-05T08:23:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.17337","created_at":"2026-07-05T08:23:43Z"},{"alias_kind":"pith_short_12","alias_value":"WU3BLV3I2FUP","created_at":"2026-07-05T08:23:43Z"},{"alias_kind":"pith_short_16","alias_value":"WU3BLV3I2FUPGPFZ","created_at":"2026-07-05T08:23:43Z"},{"alias_kind":"pith_short_8","alias_value":"WU3BLV3I","created_at":"2026-07-05T08:23:43Z"}],"graph_snapshots":[{"event_id":"sha256:135dbb9dcc1070288d23b72d4a988f26a2d67d43e66ed9456bf84e9a6f0e0360","target":"graph","created_at":"2026-07-05T08:23:43Z","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/2405.17337/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Knowledge-based question answering (KBQA) is widely used in many scenarios that necessitate domain knowledge. Large language models (LLMs) bring opportunities to KBQA, while their costs are significantly higher and absence of domain-specific knowledge during pre-training. We are motivated to combine LLMs and prior small models on knowledge graphs (KGMs) for both inferential accuracy and cost saving. However, it remains challenging since accuracy and cost are not readily combined in the optimization as two distinct metrics. It is also laborious for model selection since different models excel i","authors_text":"Chuang Zhou, Daochen Zha, Hao Chen, Junnan Dong, Qinggang Zhang, Xiao Huang","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-05-27T16:37:34Z","title":"Cost-efficient Knowledge-based Question Answering with Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.17337","kind":"arxiv","version":1},"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":"sha256:8119623a4b019523fd00dedff08fd8e9d8a8c910fb1cc757fb852c181b1feff7","target":"record","created_at":"2026-07-05T08:23:43Z","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":"a4d908d7603ecece9bb9bd3d1c3145bd7ebe33d5bbd220ea04f59c162022c5b8","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-05-27T16:37:34Z","title_canon_sha256":"6e2b7bb29002bcb5cbec5b9af7b0fc46d8566a455cd5ef57a1ab00896e964761"},"schema_version":"1.0","source":{"id":"2405.17337","kind":"arxiv","version":1}},"canonical_sha256":"b53615d768d168f33cb96460992cdb21d35e0e1a6e4a38bbacc8ea540c832cf6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b53615d768d168f33cb96460992cdb21d35e0e1a6e4a38bbacc8ea540c832cf6","first_computed_at":"2026-07-05T08:23:43.065970Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:23:43.065970Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"b6PyYhpAzROLC5+GHi5ANicH7NLOvdTCZ8LDhHDjanTtf90hRw3gilkhKcNxrFngosTlQADf42M6qnrqfw6oAA==","signature_status":"signed_v1","signed_at":"2026-07-05T08:23:43.066474Z","signed_message":"canonical_sha256_bytes"},"source_id":"2405.17337","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8119623a4b019523fd00dedff08fd8e9d8a8c910fb1cc757fb852c181b1feff7","sha256:135dbb9dcc1070288d23b72d4a988f26a2d67d43e66ed9456bf84e9a6f0e0360"],"state_sha256":"85d8b2db06dcf55eb1d4853252924b63408fa742205b0be6db268d99e8c0ab2a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Lv9qf52Wn5uyHa0kWHn9Kh7IkCULAbcHCCGMStDVRFTl6WCLYCFJpHLbfLGIJ02v5JNm4xgRrqIEU6AerAjGBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T07:58:41.719249Z","bundle_sha256":"44573658259054de27e47ffe4a0f50b5aa49eccd4646b34f121f10dafb0e7433"}}