{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:LO4WNHZOVFOXG4ZTLIOJPOD2BM","short_pith_number":"pith:LO4WNHZO","schema_version":"1.0","canonical_sha256":"5bb9669f2ea95d7373335a1c97b87a0b112e322e6be3d4b36bc5e87221cb43bf","source":{"kind":"arxiv","id":"2310.07343","version":1},"attestation_state":"computed","paper":{"title":"How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Jun Wang, Ling Chen, Meng Fang, Mohammad-Reza Namazi-Rad, Zihan Zhang","submitted_at":"2023-10-11T09:46:32Z","abstract_excerpt":"Although large language models (LLMs) are impressive in solving various tasks, they can quickly be outdated after deployment. Maintaining their up-to-date status is a pressing concern in the current era. This paper provides a comprehensive review of recent advances in aligning LLMs with the ever-changing world knowledge without re-training from scratch. We categorize research works systemically and provide in-depth comparisons and discussion. We also discuss existing challenges and highlight future directions to facilitate research in this field. We release the paper list at https://github.com"},"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":"2310.07343","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-10-11T09:46:32Z","cross_cats_sorted":[],"title_canon_sha256":"e0f25aeefb2a427c581d7c7bcace0626ac74e446874bd549df0fddbb29e82a36","abstract_canon_sha256":"c11097db59d7f8aa044462ede0b751795a72c137ab9a4199859ba05c3368cf4f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:59:44.149863Z","signature_b64":"ZtXbCPO0Qa+czscz+W1RmTwYI5JghIsJkMevgu4tJQqDlN9vjvHv0bMYkw37zFBK1HRlagpbRfBYbY/oQ6hJDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5bb9669f2ea95d7373335a1c97b87a0b112e322e6be3d4b36bc5e87221cb43bf","last_reissued_at":"2026-07-05T06:59:44.149264Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:59:44.149264Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Jun Wang, Ling Chen, Meng Fang, Mohammad-Reza Namazi-Rad, Zihan Zhang","submitted_at":"2023-10-11T09:46:32Z","abstract_excerpt":"Although large language models (LLMs) are impressive in solving various tasks, they can quickly be outdated after deployment. Maintaining their up-to-date status is a pressing concern in the current era. This paper provides a comprehensive review of recent advances in aligning LLMs with the ever-changing world knowledge without re-training from scratch. We categorize research works systemically and provide in-depth comparisons and discussion. We also discuss existing challenges and highlight future directions to facilitate research in this field. We release the paper list at https://github.com"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.07343","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/2310.07343/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":"2310.07343","created_at":"2026-07-05T06:59:44.149342+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.07343v1","created_at":"2026-07-05T06:59:44.149342+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.07343","created_at":"2026-07-05T06:59:44.149342+00:00"},{"alias_kind":"pith_short_12","alias_value":"LO4WNHZOVFOX","created_at":"2026-07-05T06:59:44.149342+00:00"},{"alias_kind":"pith_short_16","alias_value":"LO4WNHZOVFOXG4ZT","created_at":"2026-07-05T06:59:44.149342+00:00"},{"alias_kind":"pith_short_8","alias_value":"LO4WNHZO","created_at":"2026-07-05T06:59:44.149342+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2508.15411","citing_title":"Foundational Design Principles and Patterns for Building Robust and Adaptive GenAI-Native Systems","ref_index":68,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LO4WNHZOVFOXG4ZTLIOJPOD2BM","json":"https://pith.science/pith/LO4WNHZOVFOXG4ZTLIOJPOD2BM.json","graph_json":"https://pith.science/api/pith-number/LO4WNHZOVFOXG4ZTLIOJPOD2BM/graph.json","events_json":"https://pith.science/api/pith-number/LO4WNHZOVFOXG4ZTLIOJPOD2BM/events.json","paper":"https://pith.science/paper/LO4WNHZO"},"agent_actions":{"view_html":"https://pith.science/pith/LO4WNHZOVFOXG4ZTLIOJPOD2BM","download_json":"https://pith.science/pith/LO4WNHZOVFOXG4ZTLIOJPOD2BM.json","view_paper":"https://pith.science/paper/LO4WNHZO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.07343&json=true","fetch_graph":"https://pith.science/api/pith-number/LO4WNHZOVFOXG4ZTLIOJPOD2BM/graph.json","fetch_events":"https://pith.science/api/pith-number/LO4WNHZOVFOXG4ZTLIOJPOD2BM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LO4WNHZOVFOXG4ZTLIOJPOD2BM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LO4WNHZOVFOXG4ZTLIOJPOD2BM/action/storage_attestation","attest_author":"https://pith.science/pith/LO4WNHZOVFOXG4ZTLIOJPOD2BM/action/author_attestation","sign_citation":"https://pith.science/pith/LO4WNHZOVFOXG4ZTLIOJPOD2BM/action/citation_signature","submit_replication":"https://pith.science/pith/LO4WNHZOVFOXG4ZTLIOJPOD2BM/action/replication_record"}},"created_at":"2026-07-05T06:59:44.149342+00:00","updated_at":"2026-07-05T06:59:44.149342+00:00"}