Research opportunity · AI in the research apparatus
Does asking in Greek change how AI "silicon samples" answer as Greek respondents?
In short: has this been done?
Related work: Partly addressed. Related studies cover parts of this question; what they leave open is set out below. Only a first-pass search has been done, so it is not yet known whether a fuller search would find a study that does the whole design.
How far checked: First-pass screen only. A first-pass AI web search on 18 October 2026. No deeper check yet. Every search and check so far was done by AI; no person or domain expert has checked them yet.
The searches, the nearest work found and what the search could not reach
Follow this opportunity: Atom feed of RO-LD07-11 updates (new versions, and revisions of the claims it rests on).
Question
When a language model answers a few European Social Survey Round 11 questions as Greek respondents of a given age, sex, education and region, do its answers come closer to or move further from those of the Greek respondents when the prompt is in Greek rather than in English, overall and across groups?
Gap type: Geographic or language coverageUndergraduate thesisLow costRelated work: Partly addressed
Why it matters
The study that named silicon samples found close agreement for US vote choice when the model was given respondents' own partisan backstories. LD-07 also records that matching averages can hide failures on variance and subgroups, that studies against Chinese survey data found only partial reproduction, and that proprietary models change over time. Greece is already covered. A 2026 cross-national study reports ESS results for Greece from English-language prompts, and a 2024 study that simulated 548 Greek participants in a populism framing experiment found that prompting in Greek greatly reduced the simulation's fidelity. No study SocioLens found tests whether prompting in Greek rather than English changes how well a model reproduces Greek answers to survey attitude items such as those in the ESS. A small, documented comparison of the two prompt languages on such items shows whether that finding carries over from an experiment to a survey, and is an undergraduate-sized, single-country slice of RO-LD07-08.
Evidence that motivates it
- In the study that introduced "silicon samples", GPT-3 conditioned on the demographic and political backstories of real US survey respondents reproduced their two-party presidential vote closely (tetrachoric correlations of 0.90, 0.92 and 0.94 for 2012, 2016 and 2020) and the pattern of associations among 2016 survey answers; the authors called this "algorithmic fidelity" and said researchers can use such samples once it has been established for a given model and domain.
How this study uses it: GPT-3 given the backstories of US survey respondents reproduced their vote choice closely, and the authors said researchers can use such samples once this fidelity has been established for a given model and domain. - Synthetic respondents can match selected survey averages while failing to reproduce real-world variance, regression relationships, individual structure and subgroup heterogeneity.
How this study uses it: Synthetic respondents can match selected survey averages while failing to reproduce variance, regression relationships and subgroup differences. - Chinese-language silicon-sample studies benchmarked against CGSS data find partial macro-level reproduction alongside representational bias, micro/macro/meso instability and cross-model differences; current systems do not warrant replacement of human respondents.
How this study uses it: Chinese-language silicon-sample studies benchmarked against national survey data find partial reproduction alongside representational bias and differences between models. - Model identity is time-indexed: proprietary services can change materially across deployed snapshots, so exact version and date are part of the measurement protocol.
How this study uses it: Proprietary models change across deployed snapshots, so the model version and date are part of the measurement.
A possible design
Choose five to eight ESS Round 11 attitude items with clear response scales (for example trust in parliament, satisfaction with democracy or views on immigration). Draw a random sample of about 300 Greek respondents and build a short profile for each from their age, sex, education and region only, never from their attitudes. Ask one openly available model, with its version and the date recorded, to answer all the chosen items in a single prompt per profile, in a fixed order, once with the prompt in Greek and once in English (about 600 queries in all). Compare the answer distributions and the differences between groups (for example by age) with the weighted Greek ESS data for each prompt language, and report where the Greek prompt brings the model closer to, or further from, the human pattern than the English prompt. The replication files of the 2026 cross-national study give English-prompt results for Greece from two other models, which can serve as a check on the English arm.
Data needed
ESS Round 11 data for Greece (free after registration on the ESS Data Portal); access to one language model through a free tier, a university licence or a small model run locally; a spreadsheet or a short R or Python script. Optionally, the replication files of the 2026 cross-national study (https://github.com/chuyao-wang/silicon-replication).
Feasibility
Undergraduate thesis. Low; free survey data and about 600 model queries, which a free or low-cost tier or a small local model can handle. Needs basic data handling and descriptive statistics at the level of a quantitative methods course, and a supervisor comfortable with a short script. The result describes one model at one date in one country; it cannot show whether silicon samples work in general.
Proposal pack: this page and its references on one printable page, with BibTeX.
Has this been done?
Partly done already: related work covers some of this question (first-pass screen of 18 October 2026; not yet checked in depth).
A 2026 preprint tests silicon sampling against ESS Round 11 in 30 countries with two open-weight models and finds moderate, uneven agreement at country level and negligible agreement for individuals. Its replication files include Greece, with English-language prompts. A 2024 study of 15 countries, Greece among them with 548 simulated participants, compared prompt languages in a populism framing experiment and found that Greek prompting greatly reduced fidelity for the framing effects. What remains open is narrower. No study was found that compares Greek with English prompting on ESS attitude items against Greek ESS respondents, or with a current model. The search was web-based and partial.
How strong this evidence is
- Who searched and checked: no checker is recorded beyond the first-pass screen, which was part of the AI drafting (Claude (SocioLens Project Lead), AI-drafted from the LD-07 debate as an undergraduate-scale project, after the 17 October 2026 reader tests found no opportunity a final-year student could take on). Every check so far was done by AI; no person or domain expert has checked it yet.
- What was read: 1 of the 2 nearest works below was judged from an abstract, summary or search listing only; it is marked. Not every entry says how much of the work was read.
- Where the search could not reach: Screened by three web searches and arXiv records on the date shown. On 19 October 2026, after the Owner's review pointed to them, the replication files of the 30-country study were read, showing that it covers Greece, and the 2024 study was read on its arXiv HTML page; the full text of the 30-country study was not read. Absence of a hit is not proof that the work does not exist.
- Before building a project on it, run your own search in a scholarly database such as Scopus, Web of Science, Google Scholar or SSRN.
Nearest work found
- Wang, "Silicon sampling answers with country-level assumptions, not individual attitudes: Cross-national evidence from the European Social Survey", arXiv:2609.16395 (2026) Judged from an abstract, summary or search listing only
Abstract and replication files read (https://github.com/chuyao-wang/silicon-replication, 19 October 2026); the paper itself was not read. ESS Round 11, 30 countries, 42 items, two open-weight models (Qwen 2.5-7B and Llama 3.1-8B); adding the country name drives country-level agreement, individual-level agreement is negligible, and averaging neighbouring countries without a model does as well. The country-level result files include Greece, with the Greek ESS mean and the model mean for every item, and the prompt templates in the generation script are in English; no Greek-language prompt was found in the files. - Kwok, Bravansky and Griffin, "Evaluating Cultural Adaptability of a Large Language Model via Simulation of Synthetic Personas", arXiv:2408.06929 (2024)
Read on the arXiv HTML page on 19 October 2026, after the Owner's review pointed to its Greek results. GPT-3.5 simulates the 7,286 participants of a 15-country populism framing experiment (Bos et al. 2020), including 548 in Greece. Naming the country of residence improved alignment with the human results; prompting in the persona's native language reduced it, and Greek and Hebrew prompting did not reach sign agreement above chance for the framing and relative deprivation effects. It is an experiment, not a survey of attitudes, and the model is GPT-3.5.
Search queries used
LLM silicon sample European Social Survey Greece synthetic respondents comparisonLLM survey simulation prompt language English versus native language country opinions bias silicon sampling non-EnglishEuropean Social Survey Round 11 participating countries Greece
Cite this page
SocioLens project. (2026, October 18). Does asking in Greek change how AI "silicon samples" answer as Greek respondents? [Research opportunity RO-LD07-11, version v1]. SocioLens. https://sociolens-web.pages.dev/opportunities/ro-ld07-11/v1/
@misc{sociolens_ro_ld07_11_v1,
author = {{SocioLens project}},
title = {Does asking in Greek change how AI "silicon samples" answer as Greek respondents?},
year = {2026},
date = {2026-10-18},
howpublished = {\url{https://sociolens-web.pages.dev/opportunities/ro-ld07-11/v1/}},
note = {Research opportunity RO-LD07-11, version v1}
}The link points to this version, whose record never changes. For a finding, cite the original study as well: SocioLens summarizes research and does not replace it.
Provenance
RO-LD07-11 v1. Drafted by Claude (SocioLens Project Lead), AI-drafted from the LD-07 debate as an undergraduate-scale project, after the 17 October 2026 reader tests found no opportunity a final-year student could take on on 2026-10-18. Not yet reviewed. Not yet reviewed by a domain expert. This is a research hypothesis, not a finding.