{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:MB7VTEYWKOS35ZGEZQSDSHJUGI","short_pith_number":"pith:MB7VTEYW","schema_version":"1.0","canonical_sha256":"607f59931653a5bee4c4cc24391d3432043aaa8aa28ab16f695bb1a27df3ad6c","source":{"kind":"arxiv","id":"2309.15427","version":2},"attestation_state":"computed","paper":{"title":"Graph Neural Prompting with Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Fang Wang, Haozhu Wang, Huan Song, Nitesh V. Chawla, Panpan Xu, Yijun Tian, Zichen Wang, Ziqing Hu","submitted_at":"2023-09-27T06:33:29Z","abstract_excerpt":"Large language models (LLMs) have shown remarkable generalization capability with exceptional performance in various language modeling tasks. However, they still exhibit inherent limitations in precisely capturing and returning grounded knowledge. While existing work has explored utilizing knowledge graphs (KGs) to enhance language modeling via joint training and customized model architectures, applying this to LLMs is problematic owing to their large number of parameters and high computational cost. Therefore, how to enhance pre-trained LLMs using grounded knowledge, e.g., retrieval-augmented"},"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":"2309.15427","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-09-27T06:33:29Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"e5c247d503d57ab2d15202af73fd585de9d67292dc51196700ef1109fd189ad5","abstract_canon_sha256":"c04c74482145538c66786dfc6fd47a65ac2e1a5d155f8d04a3d0c3e39d8ceae6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:28:45.917168Z","signature_b64":"4Y3z/UWaJO52Ruys72o6yKY/SLxRFL4a9np0/izQRC7k+h6XduUMHlS2niDcr5Rlp29xQ7oT3uINi+gQFzrAAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"607f59931653a5bee4c4cc24391d3432043aaa8aa28ab16f695bb1a27df3ad6c","last_reissued_at":"2026-07-05T07:28:45.916521Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:28:45.916521Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Graph Neural Prompting with Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Fang Wang, Haozhu Wang, Huan Song, Nitesh V. Chawla, Panpan Xu, Yijun Tian, Zichen Wang, Ziqing Hu","submitted_at":"2023-09-27T06:33:29Z","abstract_excerpt":"Large language models (LLMs) have shown remarkable generalization capability with exceptional performance in various language modeling tasks. However, they still exhibit inherent limitations in precisely capturing and returning grounded knowledge. While existing work has explored utilizing knowledge graphs (KGs) to enhance language modeling via joint training and customized model architectures, applying this to LLMs is problematic owing to their large number of parameters and high computational cost. Therefore, how to enhance pre-trained LLMs using grounded knowledge, e.g., retrieval-augmented"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.15427","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/2309.15427/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":"2309.15427","created_at":"2026-07-05T07:28:45.916610+00:00"},{"alias_kind":"arxiv_version","alias_value":"2309.15427v2","created_at":"2026-07-05T07:28:45.916610+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.15427","created_at":"2026-07-05T07:28:45.916610+00:00"},{"alias_kind":"pith_short_12","alias_value":"MB7VTEYWKOS3","created_at":"2026-07-05T07:28:45.916610+00:00"},{"alias_kind":"pith_short_16","alias_value":"MB7VTEYWKOS35ZGE","created_at":"2026-07-05T07:28:45.916610+00:00"},{"alias_kind":"pith_short_8","alias_value":"MB7VTEYW","created_at":"2026-07-05T07:28:45.916610+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2511.19078","citing_title":"GraphMind: Theorem Selection and Conclusion Generation Framework with Dynamic GNN for LLM Reasoning","ref_index":21,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MB7VTEYWKOS35ZGEZQSDSHJUGI","json":"https://pith.science/pith/MB7VTEYWKOS35ZGEZQSDSHJUGI.json","graph_json":"https://pith.science/api/pith-number/MB7VTEYWKOS35ZGEZQSDSHJUGI/graph.json","events_json":"https://pith.science/api/pith-number/MB7VTEYWKOS35ZGEZQSDSHJUGI/events.json","paper":"https://pith.science/paper/MB7VTEYW"},"agent_actions":{"view_html":"https://pith.science/pith/MB7VTEYWKOS35ZGEZQSDSHJUGI","download_json":"https://pith.science/pith/MB7VTEYWKOS35ZGEZQSDSHJUGI.json","view_paper":"https://pith.science/paper/MB7VTEYW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2309.15427&json=true","fetch_graph":"https://pith.science/api/pith-number/MB7VTEYWKOS35ZGEZQSDSHJUGI/graph.json","fetch_events":"https://pith.science/api/pith-number/MB7VTEYWKOS35ZGEZQSDSHJUGI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MB7VTEYWKOS35ZGEZQSDSHJUGI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MB7VTEYWKOS35ZGEZQSDSHJUGI/action/storage_attestation","attest_author":"https://pith.science/pith/MB7VTEYWKOS35ZGEZQSDSHJUGI/action/author_attestation","sign_citation":"https://pith.science/pith/MB7VTEYWKOS35ZGEZQSDSHJUGI/action/citation_signature","submit_replication":"https://pith.science/pith/MB7VTEYWKOS35ZGEZQSDSHJUGI/action/replication_record"}},"created_at":"2026-07-05T07:28:45.916610+00:00","updated_at":"2026-07-05T07:28:45.916610+00:00"}