{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:E4IATBCMBPPCI5SWNVTPQW7EUG","short_pith_number":"pith:E4IATBCM","schema_version":"1.0","canonical_sha256":"271009844c0bde2476566d66f85be4a18e49a7f1c005108d3906e8399c1c6b4d","source":{"kind":"arxiv","id":"2303.03103","version":1},"attestation_state":"computed","paper":{"title":"Towards Zero-Shot Functional Compositionality of Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Hangyeol Yu, Hyeongdon Moon, Jamin Shin, Juneyoung Park, Myeongho Jeong, Seungtaek Choi","submitted_at":"2023-03-06T13:15:25Z","abstract_excerpt":"Large Pre-trained Language Models (PLM) have become the most desirable starting point in the field of NLP, as they have become remarkably good at solving many individual tasks. Despite such success, in this paper, we argue that current paradigms of working with PLMs are neglecting a critical aspect of modeling human intelligence: functional compositionality. Functional compositionality - the ability to compose learned tasks - has been a long-standing challenge in the field of AI (and many other fields) as it is considered one of the hallmarks of human intelligence. An illustrative example of s"},"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":"2303.03103","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-03-06T13:15:25Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"551f464791f8d541fa50d2e7ec844a2a2f2cfbd4eb7dc442854699ceeaf15a1b","abstract_canon_sha256":"e11ce76fd225225bd32534e6ae57bb261c20b8abad7a40d86f613a1a9aa27c75"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:48:19.178355Z","signature_b64":"OYnD0TgoSJDeVvwKq48H6OEl2W5x6UOzsf4Y2G8VNIH70rz8HTicp0KxruwHU8X2iBBQoekzMFd5ThmfyvNLAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"271009844c0bde2476566d66f85be4a18e49a7f1c005108d3906e8399c1c6b4d","last_reissued_at":"2026-07-05T05:48:19.177970Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:48:19.177970Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards Zero-Shot Functional Compositionality of Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Hangyeol Yu, Hyeongdon Moon, Jamin Shin, Juneyoung Park, Myeongho Jeong, Seungtaek Choi","submitted_at":"2023-03-06T13:15:25Z","abstract_excerpt":"Large Pre-trained Language Models (PLM) have become the most desirable starting point in the field of NLP, as they have become remarkably good at solving many individual tasks. Despite such success, in this paper, we argue that current paradigms of working with PLMs are neglecting a critical aspect of modeling human intelligence: functional compositionality. Functional compositionality - the ability to compose learned tasks - has been a long-standing challenge in the field of AI (and many other fields) as it is considered one of the hallmarks of human intelligence. An illustrative example of s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.03103","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/2303.03103/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":"2303.03103","created_at":"2026-07-05T05:48:19.178026+00:00"},{"alias_kind":"arxiv_version","alias_value":"2303.03103v1","created_at":"2026-07-05T05:48:19.178026+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.03103","created_at":"2026-07-05T05:48:19.178026+00:00"},{"alias_kind":"pith_short_12","alias_value":"E4IATBCMBPPC","created_at":"2026-07-05T05:48:19.178026+00:00"},{"alias_kind":"pith_short_16","alias_value":"E4IATBCMBPPCI5SW","created_at":"2026-07-05T05:48:19.178026+00:00"},{"alias_kind":"pith_short_8","alias_value":"E4IATBCM","created_at":"2026-07-05T05:48:19.178026+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2510.01685","citing_title":"How Do Language Models Compose Functions?","ref_index":45,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/E4IATBCMBPPCI5SWNVTPQW7EUG","json":"https://pith.science/pith/E4IATBCMBPPCI5SWNVTPQW7EUG.json","graph_json":"https://pith.science/api/pith-number/E4IATBCMBPPCI5SWNVTPQW7EUG/graph.json","events_json":"https://pith.science/api/pith-number/E4IATBCMBPPCI5SWNVTPQW7EUG/events.json","paper":"https://pith.science/paper/E4IATBCM"},"agent_actions":{"view_html":"https://pith.science/pith/E4IATBCMBPPCI5SWNVTPQW7EUG","download_json":"https://pith.science/pith/E4IATBCMBPPCI5SWNVTPQW7EUG.json","view_paper":"https://pith.science/paper/E4IATBCM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2303.03103&json=true","fetch_graph":"https://pith.science/api/pith-number/E4IATBCMBPPCI5SWNVTPQW7EUG/graph.json","fetch_events":"https://pith.science/api/pith-number/E4IATBCMBPPCI5SWNVTPQW7EUG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/E4IATBCMBPPCI5SWNVTPQW7EUG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/E4IATBCMBPPCI5SWNVTPQW7EUG/action/storage_attestation","attest_author":"https://pith.science/pith/E4IATBCMBPPCI5SWNVTPQW7EUG/action/author_attestation","sign_citation":"https://pith.science/pith/E4IATBCMBPPCI5SWNVTPQW7EUG/action/citation_signature","submit_replication":"https://pith.science/pith/E4IATBCMBPPCI5SWNVTPQW7EUG/action/replication_record"}},"created_at":"2026-07-05T05:48:19.178026+00:00","updated_at":"2026-07-05T05:48:19.178026+00:00"}