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However, most evaluations rely on distributional comparisons rather than individual-level prediction, which risks conflating pattern matching with coherent respondent-level prediction. We propose cross-survey transfer, a more rigorous evaluation framework in which an LLM is given a respondent's answers to one set of questions and must predict their answers to entirely different questions from the same survey. Using data from the Taiwan Election and Democratization Study (TEDS) 2024, three open-weight LLMs (27B-120B parameters), and supervised machine learning baselines, we find that: (1) zero-shot LLMs achieve 52% accuracy on genuinely unseen items, closing to within 6 percentage points (pp) of a supervised random forest trained on same-population data; (2) a stable const","title":"Silicon Sampling via Cross-Survey Transfer","url":"https://arxiv.org/abs/2607.03091","vendor":"arxiv_cs_ai"},"summary":"arXiv:2607.03091v1 Announce Type: new \nAbstract: Silicon sampling-using large language models (LLMs) to simulate human survey respondents-has emerged as a promising approach for augmenting traditional survey research. However, most evaluations rely on distributional comparisons rather than individual-level prediction, which risks conflating pattern matching with coherent respondent-level prediction. We propose cross-survey transfer, a more rigorous evaluation framework in which an LLM is given a respondent's answers to one set of questions and must predict their answers to entirely different questions from the same survey. Using data from the Taiwan Election and Democratization Study (TEDS) 2024, three open-weight LLMs (27B-120B parameters), and supervised machine learning baselines, we find that: (1) zero-shot LLMs achieve 52% accuracy on genuinely unseen items, closing to within 6 percentage points (pp) of a supervised random forest trained on same-population data; (2) a stable const","title":"Silicon Sampling via Cross-Survey Transfer","vendor":"arxiv_cs_ai"},"confidence":{"method":"deterministic"},"decision":"POSITIVE","issued_at":"2026-07-07T04:43:08Z","notes":"Spider vendor_press (news) news.vendor_press.v1","object":{"captured_by":"crovia.spider.vendor_press","primary_source_url":"https://arxiv.org/abs/2607.03091"},"predecessors":[],"schema":"crovia.axiom.v1","signature":"ed25519:4ce1a99e7dd8b9cf8ec24cb07ef59317545d3f4c6bb06fe06bee825b7d96a0dcc9bda6c5c141ed3a0b39fec454fd1f988982ad0c69c0385dc9d4c87365964d0d","signer":"crovia.substrate","subject":{"observed_at":"2026-07-07T04:43:08Z","source_collector":"spider:vendor_press","target_id":"https://arxiv.org/abs/2607.03091"},"tsa":{"authority":"crovia.substrate.bootstrap","rfc3161_token":"{\"kind\":\"crovia.bootstrap.tsa\",\"source_jsonl\":\"/opt/crovia/spider/data/news/vendor_press_v1.jsonl\",\"source_seal_merkle_root\":\"spider_vendor_press_v1\",\"upgrade_path\":\"Sessione H \\u2014 OpenTimestamps weekly anchor\"}"},"zk_mode":"clear","zk_proof":null},"ledger":{"leaf_hash":"7618bd43c6b6c8be51ebb73869a4b99d2b3af2be399dca62463e94f1a6919067","leaf_index":288725,"ledger_path":"/opt/crovia/substrate/axiom_ledger.jsonl"},"merkle_proof":{"hash_alg":"sha256","leaf_prefix":"0x00","node_prefix":"0x01","odd_leaf_rule":"duplicate_last","path":[{"sibling":"b05b838c99ba8909dc662a8a97b3ee243e281b5b4a5b1e6788cb5a2420040ace","side":"left"},{"sibling":"6107c7a7a09ac51d4b7968ca58b6e34109bc7fd4acaed54055ae06d1fac6e330","side":"right"},{"sibling":"05ec53b45307f6a0511a477d7ba440763b551be4a9a2cc1cbcf2e37d70a9e283","side":"left"},{"sibling":"920e39a4ffc504341dcb758e1981cd32bdb0cef664858c2852775e64d248a58f","side":"right"},{"sibling":"31347ca06a4774b8956440f8193fa1632894bf12e556e7082239a53fb87cd6ce","side":"left"},{"sibling":"c76dc76bc3b8846cbe60652cbb6f294981e01febd0141a65773df572e2064658","side":"right"},{"sibling":"61f7272108de819a7a77493153b988651785961143dd7f7cbf21c3d662725b0d","side":"left"},{"sibling":"693d22f9477576140ca2120520ac7b4b633fb122e7f590d3abc01e28b77f9cfe","side":"left"},{"sibling":"2b24ae0b0e86a6bb85711be0b56eabcfea8026b8cad7364829595d5562ea5b79","side":"left"},{"sibling":"66acb8614a0400fe91b4430bfa4ca8f7f359fb95aea361dfea6a7c88a9fe24a7","side":"left"},{"sibling":"b76af82f0e95812185e25016354f4707e41c1bacfe819e4948e5e364745511e8","side":"left"},{"sibling":"175b61fd9088baa970ad449ad7fc5d7babb21d38120cfa8c28053ae9d448ac83","side":"right"},{"sibling":"aae716235efcb893a1f219dbcd5095070d08a497769fc6d50c14976aa26d5750","side":"right"},{"sibling":"a75ab4319e241beeddb1b3f5705febe0422937926c3479923ccfb0b0082fa4e3","side":"left"},{"sibling":"bd04fa605f883bfb2b81510d045b1e85e555a03da3be083619f61384dfe40ff8","side":"left"},{"sibling":"1b72ad8d12164fdf329e7871711be99d8569d140b21f94056e6962da21da9ce1","side":"right"},{"sibling":"5f5109c2bfdcc7a7e70554bba25862e2d7ce86b6b0cd48a72eb66d2eb735f321","side":"right"},{"sibling":"05fd8a05dddb2e7f72bbb5b290ca55c378f1aed709f132277908d9a5f30eb605","side":"right"},{"sibling":"1cecb7f447febd025aac272837c80de218aecc6485d2395a509b2a1f1b9c746e","side":"left"}]},"schema":"crovia.axiom_proof.v1","seal":{"first_collector_run_id":"","first_receipt_hash":"","jsonl_path":"/opt/crovia/substrate/axiom_ledger.jsonl","key_id":"430895f101d38164","last_collector_run_id":"","last_receipt_hash":"","leaf_count":289613,"merkle_root":"dc428b9d9ba248d4f93f63147bf7c700bf5be7f500cec6c3507b9df6e9401601","public_key_hex":"cf742e26f75669dc673cb5c0786a1ae23ae8ca19c347317192ce40c28a7ff25c","run_id":"hourly_json_retrofit_20260707T053701Z","schema":"crovia.seal.v1","seal_family_version":"crovia-seal-family/1","seal_kind":"substrate_batch","sealed_at":"2026-07-07T05:38:15Z","sig_algorithm":"ed25519","signature":"c468b0e183383ab71992be40bda451093e6cd8cd8efb0d26f68e135a804b287c209d12a0f4fdd95c69c835c04b78df8cb1903dee1f53d4730b36f5332a29fe05","signer_version":"1.1.0"},"trust_root":{"key_id":"430895f101d38164","public_key_hex":"cf742e26f75669dc673cb5c0786a1ae23ae8ca19c347317192ce40c28a7ff25c","signature_algorithm":"ed25519","url":"/registry/canon/TRUST_ROOT.md"},"verifier":{"spec":"/registry/canon/AXIOM_RECEIPT_v1.md","url":"/v/axm_7ae8ae396fdbf7e4ba8128fffd73306970de9ffe05f09cd023b790891da77875"}}