{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:HIW5IXQ2SCW2EA42USI6ATCAW6","short_pith_number":"pith:HIW5IXQ2","schema_version":"1.0","canonical_sha256":"3a2dd45e1a90ada2039aa491e04c40b7a26145d2375a3d5b599dec9734d3ce56","source":{"kind":"arxiv","id":"2404.00884","version":1},"attestation_state":"computed","paper":{"title":"Self-Demos: Eliciting Out-of-Demonstration Generalizability in Large Language Models","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Jun Zhao, Qi Zhang, Shichun Liu, Tao Gui, Wei He, Xuanjing Huang, Yi Lu, Yiwen Ding, Zhiheng Xi","submitted_at":"2024-04-01T03:25:06Z","abstract_excerpt":"Large language models (LLMs) have shown promising abilities of in-context learning (ICL), adapting swiftly to new tasks with only few-shot demonstrations. However, current few-shot methods heavily depend on high-quality, query-specific demos, which are often lacking. When faced with out-of-demonstration (OOD) queries, methods that rely on hand-crafted demos or external retrievers might fail. To bridge the gap between limited demos and OOD queries, we propose Self-Demos, a novel prompting method that elicits the inherent generalizability in LLMs by query-aware demo generation. The generated dem"},"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":"2404.00884","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-04-01T03:25:06Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"8f5f9f7509fa90c79cf2beedf553a6a665e5a92d162729ef4bab6ba30fc5408b","abstract_canon_sha256":"7eb17c97588a6578f98450ed044bc2555d2b1e9d51b00943aca91366b900d233"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:02:57.984749Z","signature_b64":"DeY/ijIoLNXrgat3l+THRqxB14xuwpx6sUwrLiajip9QNk4B6Ccrj5mCYKpfTE5Y2H8x/mqVJj8RBdov4UHNBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3a2dd45e1a90ada2039aa491e04c40b7a26145d2375a3d5b599dec9734d3ce56","last_reissued_at":"2026-07-05T08:02:57.984312Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:02:57.984312Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Self-Demos: Eliciting Out-of-Demonstration Generalizability in Large Language Models","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Jun Zhao, Qi Zhang, Shichun Liu, Tao Gui, Wei He, Xuanjing Huang, Yi Lu, Yiwen Ding, Zhiheng Xi","submitted_at":"2024-04-01T03:25:06Z","abstract_excerpt":"Large language models (LLMs) have shown promising abilities of in-context learning (ICL), adapting swiftly to new tasks with only few-shot demonstrations. However, current few-shot methods heavily depend on high-quality, query-specific demos, which are often lacking. When faced with out-of-demonstration (OOD) queries, methods that rely on hand-crafted demos or external retrievers might fail. To bridge the gap between limited demos and OOD queries, we propose Self-Demos, a novel prompting method that elicits the inherent generalizability in LLMs by query-aware demo generation. The generated dem"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.00884","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/2404.00884/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":"2404.00884","created_at":"2026-07-05T08:02:57.984380+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.00884v1","created_at":"2026-07-05T08:02:57.984380+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.00884","created_at":"2026-07-05T08:02:57.984380+00:00"},{"alias_kind":"pith_short_12","alias_value":"HIW5IXQ2SCW2","created_at":"2026-07-05T08:02:57.984380+00:00"},{"alias_kind":"pith_short_16","alias_value":"HIW5IXQ2SCW2EA42","created_at":"2026-07-05T08:02:57.984380+00:00"},{"alias_kind":"pith_short_8","alias_value":"HIW5IXQ2","created_at":"2026-07-05T08:02:57.984380+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2301.00234","citing_title":"A Survey on In-context Learning","ref_index":5,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HIW5IXQ2SCW2EA42USI6ATCAW6","json":"https://pith.science/pith/HIW5IXQ2SCW2EA42USI6ATCAW6.json","graph_json":"https://pith.science/api/pith-number/HIW5IXQ2SCW2EA42USI6ATCAW6/graph.json","events_json":"https://pith.science/api/pith-number/HIW5IXQ2SCW2EA42USI6ATCAW6/events.json","paper":"https://pith.science/paper/HIW5IXQ2"},"agent_actions":{"view_html":"https://pith.science/pith/HIW5IXQ2SCW2EA42USI6ATCAW6","download_json":"https://pith.science/pith/HIW5IXQ2SCW2EA42USI6ATCAW6.json","view_paper":"https://pith.science/paper/HIW5IXQ2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.00884&json=true","fetch_graph":"https://pith.science/api/pith-number/HIW5IXQ2SCW2EA42USI6ATCAW6/graph.json","fetch_events":"https://pith.science/api/pith-number/HIW5IXQ2SCW2EA42USI6ATCAW6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HIW5IXQ2SCW2EA42USI6ATCAW6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HIW5IXQ2SCW2EA42USI6ATCAW6/action/storage_attestation","attest_author":"https://pith.science/pith/HIW5IXQ2SCW2EA42USI6ATCAW6/action/author_attestation","sign_citation":"https://pith.science/pith/HIW5IXQ2SCW2EA42USI6ATCAW6/action/citation_signature","submit_replication":"https://pith.science/pith/HIW5IXQ2SCW2EA42USI6ATCAW6/action/replication_record"}},"created_at":"2026-07-05T08:02:57.984380+00:00","updated_at":"2026-07-05T08:02:57.984380+00:00"}