{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:S6Y6TFOOHC2QDAIE4IMC2BMIPS","short_pith_number":"pith:S6Y6TFOO","schema_version":"1.0","canonical_sha256":"97b1e995ce38b5018104e2182d05887c973cbf9231362782f09011acce4aade7","source":{"kind":"arxiv","id":"2202.04728","version":1},"attestation_state":"computed","paper":{"title":"Predicting Human Similarity Judgments Using Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.LG","authors_text":"Ilia Sucholutsky, Nori Jacoby, Raja Marjieh, Theodore R. Sumers, Thomas L. Griffiths","submitted_at":"2022-02-09T21:09:25Z","abstract_excerpt":"Similarity judgments provide a well-established method for accessing mental representations, with applications in psychology, neuroscience and machine learning. However, collecting similarity judgments can be prohibitively expensive for naturalistic datasets as the number of comparisons grows quadratically in the number of stimuli. One way to tackle this problem is to construct approximation procedures that rely on more accessible proxies for predicting similarity. Here we leverage recent advances in language models and online recruitment, proposing an efficient domain-general procedure for pr"},"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":"2202.04728","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-02-09T21:09:25Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"5f0b31a1597248524f708c3f6089fa6ca6eb7435ab4f5978f03a1625bf8045af","abstract_canon_sha256":"6923b58ccaf0034b17636005edd38404b47401826dba9cd51ac159e2866c122e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:55:53.316653Z","signature_b64":"Wv2UaKoQ56YJJJww2gwahM9vTqFTLxkSaNCHYREZRFRrGSH/Ghpcde2vMkeDs6a+XfLEc5zT9LKtPMWU4mkVDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"97b1e995ce38b5018104e2182d05887c973cbf9231362782f09011acce4aade7","last_reissued_at":"2026-07-05T03:55:53.316220Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:55:53.316220Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Predicting Human Similarity Judgments Using Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.LG","authors_text":"Ilia Sucholutsky, Nori Jacoby, Raja Marjieh, Theodore R. Sumers, Thomas L. Griffiths","submitted_at":"2022-02-09T21:09:25Z","abstract_excerpt":"Similarity judgments provide a well-established method for accessing mental representations, with applications in psychology, neuroscience and machine learning. However, collecting similarity judgments can be prohibitively expensive for naturalistic datasets as the number of comparisons grows quadratically in the number of stimuli. One way to tackle this problem is to construct approximation procedures that rely on more accessible proxies for predicting similarity. Here we leverage recent advances in language models and online recruitment, proposing an efficient domain-general procedure for pr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.04728","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/2202.04728/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":"2202.04728","created_at":"2026-07-05T03:55:53.316280+00:00"},{"alias_kind":"arxiv_version","alias_value":"2202.04728v1","created_at":"2026-07-05T03:55:53.316280+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.04728","created_at":"2026-07-05T03:55:53.316280+00:00"},{"alias_kind":"pith_short_12","alias_value":"S6Y6TFOOHC2Q","created_at":"2026-07-05T03:55:53.316280+00:00"},{"alias_kind":"pith_short_16","alias_value":"S6Y6TFOOHC2QDAIE","created_at":"2026-07-05T03:55:53.316280+00:00"},{"alias_kind":"pith_short_8","alias_value":"S6Y6TFOO","created_at":"2026-07-05T03:55:53.316280+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.19497","citing_title":"The time course of visuo-semantic representations in the human brain is captured by combining vision and language models","ref_index":12,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/S6Y6TFOOHC2QDAIE4IMC2BMIPS","json":"https://pith.science/pith/S6Y6TFOOHC2QDAIE4IMC2BMIPS.json","graph_json":"https://pith.science/api/pith-number/S6Y6TFOOHC2QDAIE4IMC2BMIPS/graph.json","events_json":"https://pith.science/api/pith-number/S6Y6TFOOHC2QDAIE4IMC2BMIPS/events.json","paper":"https://pith.science/paper/S6Y6TFOO"},"agent_actions":{"view_html":"https://pith.science/pith/S6Y6TFOOHC2QDAIE4IMC2BMIPS","download_json":"https://pith.science/pith/S6Y6TFOOHC2QDAIE4IMC2BMIPS.json","view_paper":"https://pith.science/paper/S6Y6TFOO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2202.04728&json=true","fetch_graph":"https://pith.science/api/pith-number/S6Y6TFOOHC2QDAIE4IMC2BMIPS/graph.json","fetch_events":"https://pith.science/api/pith-number/S6Y6TFOOHC2QDAIE4IMC2BMIPS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/S6Y6TFOOHC2QDAIE4IMC2BMIPS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/S6Y6TFOOHC2QDAIE4IMC2BMIPS/action/storage_attestation","attest_author":"https://pith.science/pith/S6Y6TFOOHC2QDAIE4IMC2BMIPS/action/author_attestation","sign_citation":"https://pith.science/pith/S6Y6TFOOHC2QDAIE4IMC2BMIPS/action/citation_signature","submit_replication":"https://pith.science/pith/S6Y6TFOOHC2QDAIE4IMC2BMIPS/action/replication_record"}},"created_at":"2026-07-05T03:55:53.316280+00:00","updated_at":"2026-07-05T03:55:53.316280+00:00"}