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You can measure whose opinions a large language model resembles, but you cannot treat one model’s answers as a direct public-opinion poll. The defensible approach is comparative: ask standardized questions from established surveys, collect many controlled responses, turn them into probability or category distributions, and compare those distributions with human results for specific countries or demographic groups. Every result must report sensitivity to wording, answer order, language, model version, sampling, and answer-extraction rules. A model may resemble one population on one topic, then diverge sharply on another; resemblance can also come from random or stereotyped behavior rather than authentic representation.
What does it mean to measure an LLM’s “opinion”?
An LLM has no verified personal identity or civic experience. In this context, an “opinion” is an observable response pattern produced when the model is given a survey item under specified conditions. Measurement asks whether that pattern is statistically similar to responses from a defined human population; it does not establish that the model believes the proposition or speaks for the people it resembles.
The unit of analysis is usually a response distribution. For a question with four choices, you might obtain the model’s share for each choice and compare it with the shares reported by a national survey. Repeated sampling is essential because temperature, random seeds, token probabilities, and decoding policies can change an answer. A single completion is an anecdote, not a population estimate.
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Whose views appear in the output depends on pretraining data, instruction tuning, reinforcement or preference feedback, safety policies, the system prompt, the language of the question, and the evaluator’s extraction rules. Human-feedback providers and benchmark samples therefore affect which perspectives are visible.
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How have researchers compared LLMs with human populations?
Several studies illustrate both the possibilities and the limits of this method.
| Study | Design | Main finding | Important qualification |
|---|---|---|---|
| GlobalOpinionQA (Anthropic, 2023) | Cross-national survey questions and answer distributions with a country-conditioned similarity measure | Default answers were closer to some U.S., European, and South American populations; country-perspective prompts shifted similarity toward the requested population. | Steering can reproduce harmful cultural stereotypes. Translating a question did not reliably make the output most similar to speakers of that language. |
| OpinionQA (Santurkar et al., ICML/PMLR, 2023) | Comparisons across 60 U.S. demographic groups on subjects such as abortion and automation | Substantial mismatch remained, roughly comparable to the Democrat–Republican divide on climate change. | Steering toward a named group did not remove the mismatch; older adults and widowed people were weakly represented. |
| Survey-method critique (Dominguez-Olmedo, Hardt, Mendler-Dünner, NeurIPS 2024) | American Community Survey questions tested on 43 language models, with answer order and labels controlled | After systematic biases were adjusted, models across the board tended toward uniformly random survey responses. | An apparent match can be an artefact of a near-uniform distribution, not evidence that a model reflects a subgroup. |
| Method review (Ma et al., EMNLP Findings 2024) | Review of question design, prompting, response extraction, comparison, and interpretation | Attitudes, values, and opinions in LLMs remain opaque, and different evaluation methods can produce different conclusions. | The review calls for interdisciplinary safeguards rather than a single preferred metric. |
| PRISM (NeurIPS 2024) | Sociodemographics and stated preferences from 1,500 participants in 75 countries linked to 8,011 live conversations with 21 LLMs | Individual ratings and census-representative samples for the United Kingdom and United States expose sample artefacts and show why feedback-provider identity matters. | Human feedback is central to alignment, so conclusions depend on who supplied that feedback. |
| Liu, Kaneko and Chu (AAAI 2026) | World Values Survey comparisons across countries, languages, and historical periods using seven instruction-tuned multilingual LLMs | Models over-aligned with a few countries and under-aligned with most. Matching prompt language to questionnaire language improved alignment with the corresponding country; models aligned more with contemporary than historical populations. | Language and time are separate experimental factors; a result in one period or language cannot be generalized to all populations. |
How do I design a valid LLM opinion-measurement study?
Use a preregistered, repeatable protocol. The sequence below keeps measurement choices visible before you see the results.
- Choose documented survey items. Record the original wording, field dates, geography, sampling design, population definition, response scale, and any weighting used by the survey publisher. Do not silently rewrite a question to make it easier for a model.
- Define reference populations. Specify countries, age bands, education groups, or other categories in advance. “People worldwide” is not a population with a known distribution; state exactly whose human data you are using.
- Preserve one faithful baseline. Present the original answer order, labels, punctuation, and language in a no-persona condition. This is the closest test of what the model does without an imposed identity.
- Add randomized controls. Run separate conditions with answer choices shuffled and labels changed where possible. Keep a record of every permutation. A large change indicates position, label, or formatting bias rather than a change in substantive opinion.
- Specify prompts before sampling. Test no-persona, demographic-persona, country-persona, and language-matched prompts as distinct treatments. A prompt such as “Answer as a resident of…” measures steerability; it does not prove authentic representation.
- Fix model and run settings. Record the exact model identifier and release date, system message, temperature, top-p or other sampling settings, seed policy, maximum output length, context, language, and API or local-runtime version. Re-run after a model update instead of treating it as the same instrument.
- Collect independent responses. Use enough samples per item to estimate a distribution, not just a majority vote. Keep raw outputs, including refusals, multiple answers, hedges, and malformed responses, so exclusion decisions can be audited.
- Extract categories transparently. Prefer constrained choices or token probabilities when available. For free text, publish the parser, normalization rules, treatment of “both,” “uncertain,” and refusals, and the number of outputs that could not be classified.
- Compare with a predeclared metric. Calculate similarity or distance between the model vector and each human reference vector. Include uncertainty intervals and sensitivity analyses rather than selecting the metric that gives the most flattering match.
- Audit coverage and provenance. Report which countries and demographic groups are absent, who provided human feedback, and whether the benchmark or panel is census-representative. A broad country list does not compensate for missing age, disability, income, or minority groups.
Which metrics can show resemblance without overstating it?
No single score answers “whose opinion is this?” Use at least one distributional distance and show the underlying vectors. Metrics should be chosen before inspecting results.
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|---|---|---|
| Total variation distance | The largest aggregate difference in probability mass between two categorical distributions. | Easy to explain, but it hides which answer choices cause the gap. |
| Jensen–Shannon divergence | A symmetric, bounded comparison that remains defined when one distribution assigns zero probability to an option. | Report the logarithm base and whether you convert the value into a similarity score. |
| Cosine similarity | Whether two response vectors point in a similar direction. | Can look high when overall levels differ; it is not a substitute for calibration. |
| Rank correlation | Whether questions or answer choices are ordered similarly across populations. | It ignores the size of percentage differences and is unsuitable for nominal choices without a meaningful order. |
| Calibration or likelihood measures | How close model probabilities are to observed frequencies, when the model exposes probabilities. | Token probabilities may reflect wording likelihood rather than a clean survey probability. |
Bootstrap the human sample when respondent-level data are available, and resample model runs separately. If only published percentages exist, carry their sampling error and weighting notes into the analysis. Report confidence or credible intervals, item-level results, and the distribution of scores across questions; a mean score can conceal polarizing failures.
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Why do prompt wording, answer order, and language change the result?
Prompt and persona effects
Country or demographic instructions can move responses toward the requested reference group. That is useful for measuring controllability, but it can also elicit a caricature. Compare the persona condition with a no-persona baseline and describe the shift as steering sensitivity, not as evidence that the model has acquired that population’s lived experience.
Answer-order and label effects
Models may favor the first, last, or most salient label, or map a letter such as “A” to a learned pattern. The NeurIPS evaluation of 43 models found that adjusting these systematic effects could leave outputs close to uniform random behavior. Always publish both the original-order and randomized-order conditions.
Language effects
Translation changes syntax, cultural references, and the training data activated by the prompt. GlobalOpinionQA found that translation alone did not reliably maximize similarity to speakers of the target language. The later multilingual study found that matching the prompt language to the questionnaire language improved alignment with the corresponding country, so language should be treated as an experimental variable rather than a cosmetic choice.
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Temperature, top-p, seed selection, truncation, and retries alter the response distribution. A parser that converts “I lean toward yes” to “yes” may produce a different result from one that records uncertainty. Freeze these rules, test them on hand-labeled outputs, and retain an “unclassifiable” category instead of forcing every answer into a choice.
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Can an LLM survey stand in for public opinion?
No. It can serve as a benchmark instrument for studying model behavior, controllability, and representation gaps, but it is not a probability sample of citizens. The model does not have a known sampling frame, response rate, nonresponse mechanism, or stable demographic identity.
- Descriptive resemblance is not truth. A model can match a population that is itself misinformed, and it can disagree with a population while giving a factually correct answer.
- Fairness is a separate question. Equal similarity scores do not show equal treatment, equal accuracy, or absence of stereotypes.
- Stability must be demonstrated. A match that disappears after shuffling choices, changing language, or updating the model is not a robust population signal.
- Coverage is consequential. If training or feedback underrepresents older, widowed, rural, low-connectivity, or minority respondents, their perspectives may be weakly visible even when a headline country score looks good.
- Historical comparisons need special care. The AAAI study found stronger alignment with contemporary than historical populations; this may reflect training-data recency rather than social change in any simple sense.
How should I interpret “whose opinion” results?
Use a profile, not a single winner. For each population, show the item-level distance, uncertainty, prompt condition, language, and robustness to answer-order randomization. If several groups are statistically indistinguishable, report a tie or cluster rather than inventing a precise identity.
Separate three claims in your write-up:
- Behavioral claim: under the stated prompt and settings, the model’s response vector was closer to one reference distribution than to others.
- Mechanistic or procedural claim: the result changed when you altered language, persona, ordering, sampling, or extraction.
- Normative claim: whether the behavior is desirable, fair, accurate, or representative. This requires separate evidence and should never be inferred from similarity alone.
Describe uncertainty at the level readers can inspect. Include the number of model draws, missing or refused responses, survey field dates, weighting, and all preprocessing. Archive prompts and raw outputs where privacy and licensing allow; otherwise publish hashes, schemas, and a reproducible extraction script.
What are the most common failure modes?
A single completion is treated as a vote
Fix: sample repeatedly under fixed settings and report the full choice distribution.
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The model is forced to choose when it expresses uncertainty
Fix: allow “uncertain,” “does not apply,” and refusal categories, then analyze them rather than discarding them.
Only one prompt or language is tested
Fix: use a factorial design that separates persona, language, and ordering effects.
A favorable metric is chosen after seeing the data
Fix: preregister the metric, aggregation rule, exclusions, and tie policy.
Human feedback is described as universal
Fix: document the feedback providers and benchmark participants, including geography and demographics. PRISM demonstrates why the composition of those contributors can shape observed alignment.
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Survey percentages from different years are compared as if simultaneous
Fix: carry field dates into every table and analyze temporal drift separately from country differences.
What should a reproducible report contain?
A reader should be able to reconstruct the measurement without guessing. Include:
- Survey name, item wording, field dates, geography, population, sample design, weighting, and response scale.
- Model name and version, release date, provider or runtime, system prompt, user prompt, language, decoding settings, seed policy, and number of runs.
- Original and randomized answer-order conditions, label permutations, persona conditions, and any translation or back-translation process.
- Raw-output retention policy, category parser, handling of refusals and ambiguity, and counts excluded at each stage.
- Predeclared distance or similarity metrics, uncertainty method, multiple-comparison policy, and sensitivity results.
- Coverage limits, missing demographic groups, feedback-provider provenance, licensing constraints, and an explicit separation of descriptive resemblance from fairness, accuracy, and normative judgment.
Following this standard turns an eye-catching “the model thinks like country X” claim into a testable statement about a particular model, question set, language, prompt, and date.
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Frequently Asked Questions
Do I need access to token probabilities to measure LLM opinions?
No. Repeated categorical samples can estimate response frequencies when probabilities are unavailable. Probability access is helpful for calibration, but it introduces its own wording and tokenizer effects, so report how those probabilities were mapped to survey choices.
How many model responses should I collect per question?
There is no universal number; it depends on the precision you need and how variable the decoding is. Pilot a subset, estimate the variance, and choose a sample size that gives a suitably narrow interval for the smallest difference you intend to interpret.
Can I compare two model versions released months apart?
Yes, if you freeze the survey items, prompts, language, decoding policy, and extraction code. Treat the release date as an experimental factor, because a version change can alter alignment even when the model name is similar.
How do I test whether a country prompt is stereotyping people?
Compare the prompted output with locally collected survey distributions and with a no-persona, language-matched condition. Inspect which items move, especially those involving cultural generalizations, and involve regional researchers or respondents when interpreting the shifts.
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