{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:VROQ4FEYJMA45NETBERQZTTTPL","short_pith_number":"pith:VROQ4FEY","schema_version":"1.0","canonical_sha256":"ac5d0e14984b01ceb49309230cce737aef1968cf573fffd7880897c22515a280","source":{"kind":"arxiv","id":"2410.03122","version":1},"attestation_state":"computed","paper":{"title":"RIPPLECOT: Amplifying Ripple Effect of Knowledge Editing in Language Models via Chain-of-Thought In-Context Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Yijiang Li, Yinzhi Cao, Yuchen Yang, Zihao Zhao","submitted_at":"2024-10-04T03:37:36Z","abstract_excerpt":"The ripple effect poses a significant challenge in knowledge editing for large language models. Namely, when a single fact is edited, the model struggles to accurately update the related facts in a sequence, which is evaluated by multi-hop questions linked to a chain of related facts. Recent strategies have moved away from traditional parameter updates to more flexible, less computation-intensive methods, proven to be more effective in addressing the ripple effect. In-context learning (ICL) editing uses a simple demonstration `Imagine that + new fact` to guide LLMs, but struggles with complex "},"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":"2410.03122","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-10-04T03:37:36Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"3be1b1e3c1d689cfd7d380005d9513b6a048fabd1cb39a3e1f3ae73d6a7c8cd5","abstract_canon_sha256":"8e2aaf17add0abc1354858e5eed72dda264c0a78ed96f1a765badf8eb36b6c4d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:15:44.833146Z","signature_b64":"A6vKoQS+/HKzLGsjbZMkxpkB4p49iaJdo0h12hWkqEA1jst0T/MjsZFVjJEWgfhtow/ikoxRFfvMP0Y0TQjKAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ac5d0e14984b01ceb49309230cce737aef1968cf573fffd7880897c22515a280","last_reissued_at":"2026-07-05T09:15:44.832662Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:15:44.832662Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"RIPPLECOT: Amplifying Ripple Effect of Knowledge Editing in Language Models via Chain-of-Thought In-Context Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Yijiang Li, Yinzhi Cao, Yuchen Yang, Zihao Zhao","submitted_at":"2024-10-04T03:37:36Z","abstract_excerpt":"The ripple effect poses a significant challenge in knowledge editing for large language models. Namely, when a single fact is edited, the model struggles to accurately update the related facts in a sequence, which is evaluated by multi-hop questions linked to a chain of related facts. Recent strategies have moved away from traditional parameter updates to more flexible, less computation-intensive methods, proven to be more effective in addressing the ripple effect. In-context learning (ICL) editing uses a simple demonstration `Imagine that + new fact` to guide LLMs, but struggles with complex "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.03122","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/2410.03122/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":"2410.03122","created_at":"2026-07-05T09:15:44.832721+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.03122v1","created_at":"2026-07-05T09:15:44.832721+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.03122","created_at":"2026-07-05T09:15:44.832721+00:00"},{"alias_kind":"pith_short_12","alias_value":"VROQ4FEYJMA4","created_at":"2026-07-05T09:15:44.832721+00:00"},{"alias_kind":"pith_short_16","alias_value":"VROQ4FEYJMA45NET","created_at":"2026-07-05T09:15:44.832721+00:00"},{"alias_kind":"pith_short_8","alias_value":"VROQ4FEY","created_at":"2026-07-05T09:15:44.832721+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.05023","citing_title":"Scaling Expert Feedback with Reflective Edit Propagation in Compositional Knowledge Bases","ref_index":18,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VROQ4FEYJMA45NETBERQZTTTPL","json":"https://pith.science/pith/VROQ4FEYJMA45NETBERQZTTTPL.json","graph_json":"https://pith.science/api/pith-number/VROQ4FEYJMA45NETBERQZTTTPL/graph.json","events_json":"https://pith.science/api/pith-number/VROQ4FEYJMA45NETBERQZTTTPL/events.json","paper":"https://pith.science/paper/VROQ4FEY"},"agent_actions":{"view_html":"https://pith.science/pith/VROQ4FEYJMA45NETBERQZTTTPL","download_json":"https://pith.science/pith/VROQ4FEYJMA45NETBERQZTTTPL.json","view_paper":"https://pith.science/paper/VROQ4FEY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.03122&json=true","fetch_graph":"https://pith.science/api/pith-number/VROQ4FEYJMA45NETBERQZTTTPL/graph.json","fetch_events":"https://pith.science/api/pith-number/VROQ4FEYJMA45NETBERQZTTTPL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VROQ4FEYJMA45NETBERQZTTTPL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VROQ4FEYJMA45NETBERQZTTTPL/action/storage_attestation","attest_author":"https://pith.science/pith/VROQ4FEYJMA45NETBERQZTTTPL/action/author_attestation","sign_citation":"https://pith.science/pith/VROQ4FEYJMA45NETBERQZTTTPL/action/citation_signature","submit_replication":"https://pith.science/pith/VROQ4FEYJMA45NETBERQZTTTPL/action/replication_record"}},"created_at":"2026-07-05T09:15:44.832721+00:00","updated_at":"2026-07-05T09:15:44.832721+00:00"}