{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:FCCQNZYQFADV7DNC2DU6Q3RSMK","short_pith_number":"pith:FCCQNZYQ","schema_version":"1.0","canonical_sha256":"288506e71028075f8da2d0e9e86e3262b22d77ded8d59028de24cd8149f02deb","source":{"kind":"arxiv","id":"2501.00244","version":1},"attestation_state":"computed","paper":{"title":"Have We Designed Generalizable Structural Knowledge Promptings? Systematic Evaluation and Rethinking","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"BinBin Hu, Huajun Chen, Lei Liang, Lingbing Guo, Mengshu Sun, Shaokai Chen, Wen Zhang, Yajing Xu, Yichi Zhang, Zhiqiang Zhang, Zhuo Chen","submitted_at":"2024-12-31T03:20:22Z","abstract_excerpt":"Large language models (LLMs) have demonstrated exceptional performance in text generation within current NLP research. However, the lack of factual accuracy is still a dark cloud hanging over the LLM skyscraper. Structural knowledge prompting (SKP) is a prominent paradigm to integrate external knowledge into LLMs by incorporating structural representations, achieving state-of-the-art results in many knowledge-intensive tasks. However, existing methods often focus on specific problems, lacking a comprehensive exploration of the generalization and capability boundaries of SKP. This paper aims to"},"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":"2501.00244","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2024-12-31T03:20:22Z","cross_cats_sorted":[],"title_canon_sha256":"d032181a07502182deca4ce1b4375c7fc0f31fa1ea301d35d66555aa565097e6","abstract_canon_sha256":"7d7daa6ee5cc9a9e677715b6784a1cd10dac735e77ecab5245d0795a198b8d4c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:55:41.499517Z","signature_b64":"lKgPWmfi5e8jSAKXZm8osE0pGqtZHSHSH48xYMFiJS3AZ5qkWsTz2s2P1EPFQ6//MFFrZi6CJci0n8V+1MLqDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"288506e71028075f8da2d0e9e86e3262b22d77ded8d59028de24cd8149f02deb","last_reissued_at":"2026-07-05T09:55:41.499032Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:55:41.499032Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Have We Designed Generalizable Structural Knowledge Promptings? Systematic Evaluation and Rethinking","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"BinBin Hu, Huajun Chen, Lei Liang, Lingbing Guo, Mengshu Sun, Shaokai Chen, Wen Zhang, Yajing Xu, Yichi Zhang, Zhiqiang Zhang, Zhuo Chen","submitted_at":"2024-12-31T03:20:22Z","abstract_excerpt":"Large language models (LLMs) have demonstrated exceptional performance in text generation within current NLP research. However, the lack of factual accuracy is still a dark cloud hanging over the LLM skyscraper. Structural knowledge prompting (SKP) is a prominent paradigm to integrate external knowledge into LLMs by incorporating structural representations, achieving state-of-the-art results in many knowledge-intensive tasks. However, existing methods often focus on specific problems, lacking a comprehensive exploration of the generalization and capability boundaries of SKP. This paper aims to"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.00244","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/2501.00244/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":"2501.00244","created_at":"2026-07-05T09:55:41.499091+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.00244v1","created_at":"2026-07-05T09:55:41.499091+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.00244","created_at":"2026-07-05T09:55:41.499091+00:00"},{"alias_kind":"pith_short_12","alias_value":"FCCQNZYQFADV","created_at":"2026-07-05T09:55:41.499091+00:00"},{"alias_kind":"pith_short_16","alias_value":"FCCQNZYQFADV7DNC","created_at":"2026-07-05T09:55:41.499091+00:00"},{"alias_kind":"pith_short_8","alias_value":"FCCQNZYQ","created_at":"2026-07-05T09:55:41.499091+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.01293","citing_title":"Abstractive Visual Understanding of Multi-modal Structured Knowledge: A New Perspective for MLLM Evaluation","ref_index":45,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FCCQNZYQFADV7DNC2DU6Q3RSMK","json":"https://pith.science/pith/FCCQNZYQFADV7DNC2DU6Q3RSMK.json","graph_json":"https://pith.science/api/pith-number/FCCQNZYQFADV7DNC2DU6Q3RSMK/graph.json","events_json":"https://pith.science/api/pith-number/FCCQNZYQFADV7DNC2DU6Q3RSMK/events.json","paper":"https://pith.science/paper/FCCQNZYQ"},"agent_actions":{"view_html":"https://pith.science/pith/FCCQNZYQFADV7DNC2DU6Q3RSMK","download_json":"https://pith.science/pith/FCCQNZYQFADV7DNC2DU6Q3RSMK.json","view_paper":"https://pith.science/paper/FCCQNZYQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.00244&json=true","fetch_graph":"https://pith.science/api/pith-number/FCCQNZYQFADV7DNC2DU6Q3RSMK/graph.json","fetch_events":"https://pith.science/api/pith-number/FCCQNZYQFADV7DNC2DU6Q3RSMK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FCCQNZYQFADV7DNC2DU6Q3RSMK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FCCQNZYQFADV7DNC2DU6Q3RSMK/action/storage_attestation","attest_author":"https://pith.science/pith/FCCQNZYQFADV7DNC2DU6Q3RSMK/action/author_attestation","sign_citation":"https://pith.science/pith/FCCQNZYQFADV7DNC2DU6Q3RSMK/action/citation_signature","submit_replication":"https://pith.science/pith/FCCQNZYQFADV7DNC2DU6Q3RSMK/action/replication_record"}},"created_at":"2026-07-05T09:55:41.499091+00:00","updated_at":"2026-07-05T09:55:41.499091+00:00"}