{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:OFJJWXGAYMEPPTTBBMPCCE3WXX","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":"5670896ff19f4dc186b106a1b2e7c7fbc3a328bab956aa8208ff941a10e3b178","cross_cats_sorted":["cs.CL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-07T02:12:22Z","title_canon_sha256":"67e57bdd02bf62e64e1f95e3e30b34035b3dc84a2ba768996c686eba3ae0715f"},"schema_version":"1.0","source":{"id":"2410.04691","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.04691","created_at":"2026-07-05T09:16:54Z"},{"alias_kind":"arxiv_version","alias_value":"2410.04691v1","created_at":"2026-07-05T09:16:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.04691","created_at":"2026-07-05T09:16:54Z"},{"alias_kind":"pith_short_12","alias_value":"OFJJWXGAYMEP","created_at":"2026-07-05T09:16:54Z"},{"alias_kind":"pith_short_16","alias_value":"OFJJWXGAYMEPPTTB","created_at":"2026-07-05T09:16:54Z"},{"alias_kind":"pith_short_8","alias_value":"OFJJWXGA","created_at":"2026-07-05T09:16:54Z"}],"graph_snapshots":[{"event_id":"sha256:9c02ac26918cc2ff9800a7042c2f25547d01303b68e0cbf09e77a04be74bdae9","target":"graph","created_at":"2026-07-05T09:16:54Z","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/2410.04691/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Fine-tuning and in-context learning (ICL) are two prevalent methods in imbuing large language models with task-specific knowledge. It is commonly believed that fine-tuning can surpass ICL given sufficient training samples as it allows the model to adjust its internal parameters based on the data. However, this paper presents a counterintuitive finding: For tasks with implicit patterns, ICL captures these patterns significantly better than fine-tuning. We developed several datasets featuring implicit patterns, such as sequences determining answers through parity or identifying reducible terms i","authors_text":"Chak Tou Leong, Fan Wang, Luoao Deng, Qiang Zhang, Qingyu Yin, Xiaoyu Shen, Xuzheng He, Yanzhao Yan","cross_cats":["cs.CL"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-07T02:12:22Z","title":"Deeper Insights Without Updates: The Power of In-Context Learning Over Fine-Tuning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.04691","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:dd9b4f37a40c8279cc1aeddae4f0a5630aa4589ae2f8bcb4fd3edb5498ddd651","target":"record","created_at":"2026-07-05T09:16:54Z","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":"5670896ff19f4dc186b106a1b2e7c7fbc3a328bab956aa8208ff941a10e3b178","cross_cats_sorted":["cs.CL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-07T02:12:22Z","title_canon_sha256":"67e57bdd02bf62e64e1f95e3e30b34035b3dc84a2ba768996c686eba3ae0715f"},"schema_version":"1.0","source":{"id":"2410.04691","kind":"arxiv","version":1}},"canonical_sha256":"71529b5cc0c308f7ce610b1e211376bde7e67bcd76ca5d4c1684b333f02ebba3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"71529b5cc0c308f7ce610b1e211376bde7e67bcd76ca5d4c1684b333f02ebba3","first_computed_at":"2026-07-05T09:16:54.581294Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:16:54.581294Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"PI2PvzzninS0k4KJpDoxpCMPMNY1T71gJMb+XBuwM9bcvChLIpxFO87UMy30qflJUufhDdORiV1jvRKQFQbZBA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:16:54.581793Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.04691","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:dd9b4f37a40c8279cc1aeddae4f0a5630aa4589ae2f8bcb4fd3edb5498ddd651","sha256:9c02ac26918cc2ff9800a7042c2f25547d01303b68e0cbf09e77a04be74bdae9"],"state_sha256":"17c8031a3853210803696f23152ec28bf430a37b7fbfac6ab7fbf36639551e99"}