{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:M4IZD5MIGV3IFO76PGXPF27MNR","short_pith_number":"pith:M4IZD5MI","schema_version":"1.0","canonical_sha256":"671191f588357682bbfe79aef2ebec6c636083a804873962ec3c1f07936642d0","source":{"kind":"arxiv","id":"2310.20384","version":1},"attestation_state":"computed","paper":{"title":"Do large language models solve verbal analogies like children do?","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Claire E. Stevenson, Ekaterina Shutova, Han L. J. van der Maas, Mathilde ter Veen, Rochelle Choenni","submitted_at":"2023-10-31T11:49:11Z","abstract_excerpt":"Analogy-making lies at the heart of human cognition. Adults solve analogies such as \\textit{Horse belongs to stable like chicken belongs to ...?} by mapping relations (\\textit{kept in}) and answering \\textit{chicken coop}. In contrast, children often use association, e.g., answering \\textit{egg}. This paper investigates whether large language models (LLMs) solve verbal analogies in A:B::C:? form using associations, similar to what children do. We use verbal analogies extracted from an online adaptive learning environment, where 14,002 7-12 year-olds from the Netherlands solved 622 analogies in"},"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":"2310.20384","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2023-10-31T11:49:11Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"40865f6be0a3d7a5187d1f57d265e090c6d2d6acb99842c4238e4cf20870f55d","abstract_canon_sha256":"716a3f22ba3bac358405d7bd86f8e50f6e380a4cbe40afd80a6f9e97d79fb3d8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:07:29.363117Z","signature_b64":"gsZ2HA0O7nwsgTXDtoM88towEOS89dVTY3rzPQB6bZ70QQ9wbB8elFY+urm8oI3TCKywdaV4cnYn5pSfCmTXCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"671191f588357682bbfe79aef2ebec6c636083a804873962ec3c1f07936642d0","last_reissued_at":"2026-07-05T07:07:29.362707Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:07:29.362707Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Do large language models solve verbal analogies like children do?","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Claire E. Stevenson, Ekaterina Shutova, Han L. J. van der Maas, Mathilde ter Veen, Rochelle Choenni","submitted_at":"2023-10-31T11:49:11Z","abstract_excerpt":"Analogy-making lies at the heart of human cognition. Adults solve analogies such as \\textit{Horse belongs to stable like chicken belongs to ...?} by mapping relations (\\textit{kept in}) and answering \\textit{chicken coop}. In contrast, children often use association, e.g., answering \\textit{egg}. This paper investigates whether large language models (LLMs) solve verbal analogies in A:B::C:? form using associations, similar to what children do. We use verbal analogies extracted from an online adaptive learning environment, where 14,002 7-12 year-olds from the Netherlands solved 622 analogies in"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.20384","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/2310.20384/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":"2310.20384","created_at":"2026-07-05T07:07:29.362768+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.20384v1","created_at":"2026-07-05T07:07:29.362768+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.20384","created_at":"2026-07-05T07:07:29.362768+00:00"},{"alias_kind":"pith_short_12","alias_value":"M4IZD5MIGV3I","created_at":"2026-07-05T07:07:29.362768+00:00"},{"alias_kind":"pith_short_16","alias_value":"M4IZD5MIGV3IFO76","created_at":"2026-07-05T07:07:29.362768+00:00"},{"alias_kind":"pith_short_8","alias_value":"M4IZD5MI","created_at":"2026-07-05T07:07:29.362768+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.01359","citing_title":"Structural Ranking of the Cognitive Plausibility of Computational Models of Analogy and Metaphors with the Minimal Cognitive Grid","ref_index":263,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/M4IZD5MIGV3IFO76PGXPF27MNR","json":"https://pith.science/pith/M4IZD5MIGV3IFO76PGXPF27MNR.json","graph_json":"https://pith.science/api/pith-number/M4IZD5MIGV3IFO76PGXPF27MNR/graph.json","events_json":"https://pith.science/api/pith-number/M4IZD5MIGV3IFO76PGXPF27MNR/events.json","paper":"https://pith.science/paper/M4IZD5MI"},"agent_actions":{"view_html":"https://pith.science/pith/M4IZD5MIGV3IFO76PGXPF27MNR","download_json":"https://pith.science/pith/M4IZD5MIGV3IFO76PGXPF27MNR.json","view_paper":"https://pith.science/paper/M4IZD5MI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.20384&json=true","fetch_graph":"https://pith.science/api/pith-number/M4IZD5MIGV3IFO76PGXPF27MNR/graph.json","fetch_events":"https://pith.science/api/pith-number/M4IZD5MIGV3IFO76PGXPF27MNR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/M4IZD5MIGV3IFO76PGXPF27MNR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/M4IZD5MIGV3IFO76PGXPF27MNR/action/storage_attestation","attest_author":"https://pith.science/pith/M4IZD5MIGV3IFO76PGXPF27MNR/action/author_attestation","sign_citation":"https://pith.science/pith/M4IZD5MIGV3IFO76PGXPF27MNR/action/citation_signature","submit_replication":"https://pith.science/pith/M4IZD5MIGV3IFO76PGXPF27MNR/action/replication_record"}},"created_at":"2026-07-05T07:07:29.362768+00:00","updated_at":"2026-07-05T07:07:29.362768+00:00"}