{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:UZN3HRO3O2GCPUTHA3FXD54ZGS","short_pith_number":"pith:UZN3HRO3","canonical_record":{"source":{"id":"2405.06524","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2024-05-10T15:10:20Z","cross_cats_sorted":[],"title_canon_sha256":"fbce5702105a5c2f5a6cd2b66de979fc06610def4de453f17407b27cf6963e94","abstract_canon_sha256":"12bb3b587ac19816e78b269e22386f26f4e091fa5368a4b01574bfd3cca416fb"},"schema_version":"1.0"},"canonical_sha256":"a65bb3c5db768c27d26706cb71f79934b2171b8bf2c0cea934d83d9d2d1a3031","source":{"kind":"arxiv","id":"2405.06524","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.06524","created_at":"2026-07-05T08:17:50Z"},{"alias_kind":"arxiv_version","alias_value":"2405.06524v1","created_at":"2026-07-05T08:17:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.06524","created_at":"2026-07-05T08:17:50Z"},{"alias_kind":"pith_short_12","alias_value":"UZN3HRO3O2GC","created_at":"2026-07-05T08:17:50Z"},{"alias_kind":"pith_short_16","alias_value":"UZN3HRO3O2GCPUTH","created_at":"2026-07-05T08:17:50Z"},{"alias_kind":"pith_short_8","alias_value":"UZN3HRO3","created_at":"2026-07-05T08:17:50Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:UZN3HRO3O2GCPUTHA3FXD54ZGS","target":"record","payload":{"canonical_record":{"source":{"id":"2405.06524","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2024-05-10T15:10:20Z","cross_cats_sorted":[],"title_canon_sha256":"fbce5702105a5c2f5a6cd2b66de979fc06610def4de453f17407b27cf6963e94","abstract_canon_sha256":"12bb3b587ac19816e78b269e22386f26f4e091fa5368a4b01574bfd3cca416fb"},"schema_version":"1.0"},"canonical_sha256":"a65bb3c5db768c27d26706cb71f79934b2171b8bf2c0cea934d83d9d2d1a3031","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:17:50.676214Z","signature_b64":"nfmxBVr2rmHoyVtfae2IQu09uV+vZXH1FJoYzCS7CFGpInZkN4qoPCVtyl/acZ3Xz9qW5VmngsIQFxj/GcJBCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a65bb3c5db768c27d26706cb71f79934b2171b8bf2c0cea934d83d9d2d1a3031","last_reissued_at":"2026-07-05T08:17:50.675721Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:17:50.675721Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2405.06524","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":"2026-07-05T08:17:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nB0mBp64aRwj9UIFHwzRUrDxMZ0WrGF/urZ/DoE2UdFYVQgR1q3GDaodsn7aee2Nw17GzN4E0igixHLS6qPXAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T16:28:03.765009Z"},"content_sha256":"aa74b9d23af76e879b23f73ea3bcb1a7de82da030c34dc82645175d250e2c031","schema_version":"1.0","event_id":"sha256:aa74b9d23af76e879b23f73ea3bcb1a7de82da030c34dc82645175d250e2c031"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:UZN3HRO3O2GCPUTHA3FXD54ZGS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Prompting Large Language Models with Knowledge Graphs for Question Answering Involving Long-tail Facts","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Guancheng Zhou, Jeff Z. Pan, Mirella Lapata, Pavlos Vougiouklis, Sebastien Montella, Wenyu Huang","submitted_at":"2024-05-10T15:10:20Z","abstract_excerpt":"Although Large Language Models (LLMs) are effective in performing various NLP tasks, they still struggle to handle tasks that require extensive, real-world knowledge, especially when dealing with long-tail facts (facts related to long-tail entities). This limitation highlights the need to supplement LLMs with non-parametric knowledge. To address this issue, we analysed the effects of different types of non-parametric knowledge, including textual passage and knowledge graphs (KGs). Since LLMs have probably seen the majority of factual question-answering datasets already, to facilitate our analy"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.06524","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/2405.06524/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":"2026-07-05T08:17:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Txb2RS2Evceqb4dG/2Eu23jac81xTQU22eLY36/l6JxbdvnGxHvfGJKBYivfUVz8yFeLwZpm8vUL+lLLqJp1Dw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T16:28:03.765388Z"},"content_sha256":"38f2dbb11a0c09b86701173a24297fe71ea160b337ee9d3351cd888e66943c70","schema_version":"1.0","event_id":"sha256:38f2dbb11a0c09b86701173a24297fe71ea160b337ee9d3351cd888e66943c70"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UZN3HRO3O2GCPUTHA3FXD54ZGS/bundle.json","state_url":"https://pith.science/pith/UZN3HRO3O2GCPUTHA3FXD54ZGS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UZN3HRO3O2GCPUTHA3FXD54ZGS/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":"2026-07-06T16:28:03Z","links":{"resolver":"https://pith.science/pith/UZN3HRO3O2GCPUTHA3FXD54ZGS","bundle":"https://pith.science/pith/UZN3HRO3O2GCPUTHA3FXD54ZGS/bundle.json","state":"https://pith.science/pith/UZN3HRO3O2GCPUTHA3FXD54ZGS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UZN3HRO3O2GCPUTHA3FXD54ZGS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:UZN3HRO3O2GCPUTHA3FXD54ZGS","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":"12bb3b587ac19816e78b269e22386f26f4e091fa5368a4b01574bfd3cca416fb","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2024-05-10T15:10:20Z","title_canon_sha256":"fbce5702105a5c2f5a6cd2b66de979fc06610def4de453f17407b27cf6963e94"},"schema_version":"1.0","source":{"id":"2405.06524","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.06524","created_at":"2026-07-05T08:17:50Z"},{"alias_kind":"arxiv_version","alias_value":"2405.06524v1","created_at":"2026-07-05T08:17:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.06524","created_at":"2026-07-05T08:17:50Z"},{"alias_kind":"pith_short_12","alias_value":"UZN3HRO3O2GC","created_at":"2026-07-05T08:17:50Z"},{"alias_kind":"pith_short_16","alias_value":"UZN3HRO3O2GCPUTH","created_at":"2026-07-05T08:17:50Z"},{"alias_kind":"pith_short_8","alias_value":"UZN3HRO3","created_at":"2026-07-05T08:17:50Z"}],"graph_snapshots":[{"event_id":"sha256:38f2dbb11a0c09b86701173a24297fe71ea160b337ee9d3351cd888e66943c70","target":"graph","created_at":"2026-07-05T08:17:50Z","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/2405.06524/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Although Large Language Models (LLMs) are effective in performing various NLP tasks, they still struggle to handle tasks that require extensive, real-world knowledge, especially when dealing with long-tail facts (facts related to long-tail entities). This limitation highlights the need to supplement LLMs with non-parametric knowledge. To address this issue, we analysed the effects of different types of non-parametric knowledge, including textual passage and knowledge graphs (KGs). Since LLMs have probably seen the majority of factual question-answering datasets already, to facilitate our analy","authors_text":"Guancheng Zhou, Jeff Z. Pan, Mirella Lapata, Pavlos Vougiouklis, Sebastien Montella, Wenyu Huang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2024-05-10T15:10:20Z","title":"Prompting Large Language Models with Knowledge Graphs for Question Answering Involving Long-tail Facts"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.06524","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:aa74b9d23af76e879b23f73ea3bcb1a7de82da030c34dc82645175d250e2c031","target":"record","created_at":"2026-07-05T08:17:50Z","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":"12bb3b587ac19816e78b269e22386f26f4e091fa5368a4b01574bfd3cca416fb","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2024-05-10T15:10:20Z","title_canon_sha256":"fbce5702105a5c2f5a6cd2b66de979fc06610def4de453f17407b27cf6963e94"},"schema_version":"1.0","source":{"id":"2405.06524","kind":"arxiv","version":1}},"canonical_sha256":"a65bb3c5db768c27d26706cb71f79934b2171b8bf2c0cea934d83d9d2d1a3031","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a65bb3c5db768c27d26706cb71f79934b2171b8bf2c0cea934d83d9d2d1a3031","first_computed_at":"2026-07-05T08:17:50.675721Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:17:50.675721Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"nfmxBVr2rmHoyVtfae2IQu09uV+vZXH1FJoYzCS7CFGpInZkN4qoPCVtyl/acZ3Xz9qW5VmngsIQFxj/GcJBCA==","signature_status":"signed_v1","signed_at":"2026-07-05T08:17:50.676214Z","signed_message":"canonical_sha256_bytes"},"source_id":"2405.06524","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:aa74b9d23af76e879b23f73ea3bcb1a7de82da030c34dc82645175d250e2c031","sha256:38f2dbb11a0c09b86701173a24297fe71ea160b337ee9d3351cd888e66943c70"],"state_sha256":"4f5ef498e371bab365f8c08bd08ebaa3800611e36a37d3eae80d040037384cb0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4/Sa0w9XuuA7kwG/fzT6LD+FFQObiOZMDYE9ZC0GfEavoMykbAOrRi+W3YaxPC4vpRvtHs1/jJyHqaA37g8cCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T16:28:03.767362Z","bundle_sha256":"f29100b8126d6d858848e5d795fb7f949c0140ce9f4ebcd10ed0c4dc21a2d7e6"}}