{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:BXPY3WAHQNN5ZX6IIIE7BFY24X","short_pith_number":"pith:BXPY3WAH","schema_version":"1.0","canonical_sha256":"0ddf8dd807835bdcdfc84209f0971ae5e9345fcf3e546c570076c2fa8a32d578","source":{"kind":"arxiv","id":"2308.00304","version":3},"attestation_state":"computed","paper":{"title":"Skills-in-Context Prompting: Unlocking Compositionality in Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Dian Yu, Dong Yu, Jiaao Chen, Jianshu Chen, Kaiqiang Song, Xiaoman Pan, Xiaoyang Wang","submitted_at":"2023-08-01T05:54:12Z","abstract_excerpt":"We investigate how to elicit compositional generalization capabilities in large language models (LLMs). Compositional generalization empowers LLMs to solve complex problems by combining foundational skills, a critical reasoning ability akin to human intelligence. However, even the most advanced LLMs currently struggle with this form of reasoning. We examine this problem within the framework of in-context learning and find that demonstrating both foundational skills and compositional examples grounded in these skills within the same prompt context is crucial. We refer to this prompt structure a"},"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":"2308.00304","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-08-01T05:54:12Z","cross_cats_sorted":[],"title_canon_sha256":"ddabe1446dfd54a0517309adc29c5d76738f1efc75d80d7670935b3d50ceb337","abstract_canon_sha256":"74d73bac1791c03dda85bec53f47aa6e4417502607ac167961b3f7182940d9a3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:44:46.785342Z","signature_b64":"4vnb7pyAy278F0u0K0y+ZDaznHqTe4OEjYKaSuBXbWJ1vOVnZPyBcaEoV6+E5VfZSbbhoHXrIfr3LxSOyYI4Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0ddf8dd807835bdcdfc84209f0971ae5e9345fcf3e546c570076c2fa8a32d578","last_reissued_at":"2026-07-05T08:44:46.784866Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:44:46.784866Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Skills-in-Context Prompting: Unlocking Compositionality in Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Dian Yu, Dong Yu, Jiaao Chen, Jianshu Chen, Kaiqiang Song, Xiaoman Pan, Xiaoyang Wang","submitted_at":"2023-08-01T05:54:12Z","abstract_excerpt":"We investigate how to elicit compositional generalization capabilities in large language models (LLMs). Compositional generalization empowers LLMs to solve complex problems by combining foundational skills, a critical reasoning ability akin to human intelligence. However, even the most advanced LLMs currently struggle with this form of reasoning. We examine this problem within the framework of in-context learning and find that demonstrating both foundational skills and compositional examples grounded in these skills within the same prompt context is crucial. We refer to this prompt structure a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.00304","kind":"arxiv","version":3},"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/2308.00304/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":"2308.00304","created_at":"2026-07-05T08:44:46.784922+00:00"},{"alias_kind":"arxiv_version","alias_value":"2308.00304v3","created_at":"2026-07-05T08:44:46.784922+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.00304","created_at":"2026-07-05T08:44:46.784922+00:00"},{"alias_kind":"pith_short_12","alias_value":"BXPY3WAHQNN5","created_at":"2026-07-05T08:44:46.784922+00:00"},{"alias_kind":"pith_short_16","alias_value":"BXPY3WAHQNN5ZX6I","created_at":"2026-07-05T08:44:46.784922+00:00"},{"alias_kind":"pith_short_8","alias_value":"BXPY3WAH","created_at":"2026-07-05T08:44:46.784922+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.04970","citing_title":"Skill Neologisms: Towards Skill-based Continual Learning","ref_index":3,"is_internal_anchor":false},{"citing_arxiv_id":"2605.04970","citing_title":"Skill Neologisms: Towards Skill-based Continual Learning","ref_index":3,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BXPY3WAHQNN5ZX6IIIE7BFY24X","json":"https://pith.science/pith/BXPY3WAHQNN5ZX6IIIE7BFY24X.json","graph_json":"https://pith.science/api/pith-number/BXPY3WAHQNN5ZX6IIIE7BFY24X/graph.json","events_json":"https://pith.science/api/pith-number/BXPY3WAHQNN5ZX6IIIE7BFY24X/events.json","paper":"https://pith.science/paper/BXPY3WAH"},"agent_actions":{"view_html":"https://pith.science/pith/BXPY3WAHQNN5ZX6IIIE7BFY24X","download_json":"https://pith.science/pith/BXPY3WAHQNN5ZX6IIIE7BFY24X.json","view_paper":"https://pith.science/paper/BXPY3WAH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2308.00304&json=true","fetch_graph":"https://pith.science/api/pith-number/BXPY3WAHQNN5ZX6IIIE7BFY24X/graph.json","fetch_events":"https://pith.science/api/pith-number/BXPY3WAHQNN5ZX6IIIE7BFY24X/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BXPY3WAHQNN5ZX6IIIE7BFY24X/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BXPY3WAHQNN5ZX6IIIE7BFY24X/action/storage_attestation","attest_author":"https://pith.science/pith/BXPY3WAHQNN5ZX6IIIE7BFY24X/action/author_attestation","sign_citation":"https://pith.science/pith/BXPY3WAHQNN5ZX6IIIE7BFY24X/action/citation_signature","submit_replication":"https://pith.science/pith/BXPY3WAHQNN5ZX6IIIE7BFY24X/action/replication_record"}},"created_at":"2026-07-05T08:44:46.784922+00:00","updated_at":"2026-07-05T08:44:46.784922+00:00"}