[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"branding":3,"analytics":7,"article-can-ai-predict-how-youd-answer-a-survey":10,"sections":34},{"siteName":4,"siteTagline":5,"publisherName":4,"contactEmail":6},"The Revision","Tech news, decoded.","editor@therevision.news",{"gaMeasurementId":8,"adsenseClientId":9},"G-ZW2MV82GYR","ca-pub-8533917693782264",{"article":11},{"id":12,"slug":13,"title":14,"dek":15,"body_md":16,"tags_json":17,"published_at":18,"created_at":19,"updated_at":20,"status":21,"review_note":22,"review_notes":23,"image_url":22,"persona_id":22,"persona_name":22,"section":24,"tags":25,"sources":29,"feedback":33,"feedback_at":22,"cost_usd":33,"total_tokens":33},3659,"can-ai-predict-how-youd-answer-a-survey","Can AI Predict How You'd Answer a Survey?","A new study tests whether LLMs can genuinely model individual survey respondents — or just mimic population-level patterns.","A research paper on arXiv proposes a tougher benchmark for \"silicon sampling\" — the practice of using large language models to stand in for human survey respondents.\n\nMost existing evaluations check whether an LLM's outputs match the statistical distribution of a real survey population, which is a low bar. The researchers instead tested cross-survey transfer: give a model one set of a respondent's answers, then ask it to predict that same person's answers to completely different questions. Using Taiwan Election and Democratization Study data from 2024 and three open-weight models ranging from 27B to 120B parameters, they found zero-shot LLMs hit 52% accuracy on genuinely unseen items — within 6 percentage points of a supervised random forest trained on same-population data. That gap is smaller than most skeptics would have predicted.\n\nThe finding matters because silicon sampling is already being pitched as a cost-cutting substitute for expensive human panels. If the approach only works at the population level, it is a fancy way to regurgitate training data. Individual-level predictive accuracy is the harder and more honest test — and these results suggest the method clears a real, if modest, bar. Two caveats the field obsesses over — variance collapse and safety alignment effects — also turned out to be more complicated than assumed, with variance collapse showing up in supervised models too.\n\nPredictability varied wildly by topic: partisan attitudes were forecast at 67% accuracy, while sovereignty questions landed at 23%, which should give anyone pause before deploying LLMs on politically sensitive or contested terrain.","[\"ai\",\"survey-research\",\"llms\",\"social-science\"]","2026-07-07T04:00:00.000Z","2026-07-07T05:06:17.702Z","2026-07-07T05:06:20.676Z","published",null,[],"ai",[24,26,27,28],"survey-research","llms","social-science",[30],{"name":31,"url":32},"arXiv cs.AI","https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.03091",0,{"sections":35},[36,40,45,50,55,60,65,70,75,79,84,88,93,98],{"name":37,"slug":24,"count":38,"latest_published_at":39},"AI",2590,"2026-07-16T04:00:00.000Z",{"name":41,"slug":42,"count":43,"latest_published_at":44},"Security","security",294,"2026-07-15T19:59:48.000Z",{"name":46,"slug":47,"count":48,"latest_published_at":49},"Deals","deals",179,"2026-06-29T20:02:07.000Z",{"name":51,"slug":52,"count":53,"latest_published_at":54},"Policy","policy",158,"2026-07-16T00:02:48.000Z",{"name":56,"slug":57,"count":58,"latest_published_at":59},"Hardware","hardware",122,"2026-07-14T19:46:26.000Z",{"name":61,"slug":62,"count":63,"latest_published_at":64},"Consumer Tech","consumer-tech",93,"2026-07-13T13:20:48.000Z",{"name":66,"slug":67,"count":68,"latest_published_at":69},"Software","software",70,"2026-07-13T19:52:25.000Z",{"name":71,"slug":72,"count":73,"latest_published_at":74},"Science","science",66,"2026-07-10T10:29:37.000Z",{"name":76,"slug":77,"count":78,"latest_published_at":18},"Dev Tools","dev-tools",59,{"name":80,"slug":81,"count":82,"latest_published_at":83},"Gaming","gaming",41,"2026-07-09T04:00:00.000Z",{"name":85,"slug":86,"count":82,"latest_published_at":87},"Startups","startups","2026-06-29T20:55:50.000Z",{"name":89,"slug":90,"count":91,"latest_published_at":92},"General","general",29,"2026-07-10T22:28:58.000Z",{"name":94,"slug":95,"count":96,"latest_published_at":97},"Reviews","reviews",20,"2026-06-24T12:00:01.000Z",{"name":99,"slug":100,"count":101,"latest_published_at":102},"How-To","how-to",6,"2026-06-16T09:00:00.000Z"]