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To test bias in a large language model (LLM), combine three kinds of evidence: measure associations in embeddings or sentence encoders, run controlled prompts that differ only in a demographic attribute, and evaluate the model’s generated text and task outcomes. No one score establishes whether a system is biased in use. A reproducible audit connects these layers, reports uncertainty and model settings, and checks whether apparent disparities matter in realistic tasks.
Why test bias at more than one layer?
Bias can appear at different points in an LLM system. A representation may associate a group with a trait; a prompt may cause different probabilities or answers when an identity term changes; and a generated response may portray, rank, omit, refuse, or treat people differently. These are related questions, but they are not interchangeable measurements.
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An embedding association is an indirect signal. It does not by itself show that a deployed product will make a discriminatory decision. A prompt-level probability difference can reveal sensitivity to wording or identity, but may not predict behavior in a longer exchange. Generated-text scoring is closer to what a user sees, yet it depends on the chosen scoring method and can miss context. Surveys of evaluation methods therefore recommend examining bias through multiple lenses and publishing the model specifications and exact prompts or templates used.
The audit should be designed around a defined risk or use case, not a search for one universal “bias score.” The useful outcome is a set of comparable observations: what association was measured, which behavior changed, how often it changed, how uncertain the estimate is, and whether reviewers or task-based tests corroborate it.
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What should you define before running tests?
Write down the scope before collecting results. A test cannot be reproduced or interpreted if “the model” and “bias” are left undefined.
- System under test: identify the model name and version, checkpoint or API, tokenizer, context length, and any system prompt. If a model is updated or served through an API, record the version or date identifier available to you.
- Attributes and constructs: specify which demographic attributes you will test and what association or outcome matters, such as a stereotype, sentiment, regard, toxicity, refusal, or task decision. Avoid treating a single identity word as a complete stand-in for a group.
- Language and population: state the language, spelling and naming conventions, and any geographic scope. Benchmarks focused on U.S. English should not be presented as establishing behavior for other languages or populations.
- Prompt and generation settings: archive the system and user prompts, temperature, top-p, seed policy, and output or decoding limits. Record whether you compare next-token probabilities, sampled text, or both.
- Analysis plan: decide in advance which outcomes, comparisons, uncertainty intervals, and subgroup analyses you will report. If you test many attributes, templates, or metrics, document how you will handle multiple comparisons.
Keep a versioned record of prompts, data, code, configuration, and scoring procedures. This is not administrative overhead: changes in templates, decoding, model versions, or scorers can change the result and make two audits look more comparable than they are.
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WEAT- or SEAT-style tests ask whether predefined groups of words or sentences are more closely associated with one set of target concepts than another. They provide a compact, repeatable representation-level signal, not a direct estimate of harm in an application.
Build balanced sets
Choose attribute terms representing the groups of interest and target terms representing the construct being tested. For example, a study might define two attribute sets and two target sets, but the specific terms must follow the audit’s question and language. Balance the sets in size and form where possible, document every item, and explain exclusions or ambiguous terms. If the test uses sentence templates, publish the templates as well as the resulting sentences.
Report the effect and its uncertainty
Report the WEAT/SEAT-style effect size, not just a binary claim that a test “passed” or “failed.” Include permutation or bootstrap uncertainty, the number of terms or sentences in each set, template variants, and the pooling choice used to represent sentences. If multiple tests are run, state how multiple comparisons were handled. A result that shifts under reasonable template or pooling variants should be presented as sensitive to those choices.
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Interpret an elevated association as evidence about the tested representation and construct. It is not proof that the model will produce a particular answer, nor that a real-world decision will discriminate. That gap is why embedding results need to be paired with model behavior and downstream task tests.
How can you test whether prompts change model behavior?
Use counterfactual prompt pairs: two prompts with the same task, facts, syntax, and requested response, changing only the demographic attribute under examination. Differences then have a clearer interpretation than comparisons between unrelated prompts.
- Write a neutral task prompt. Specify the request and relevant facts without adding assumptions about the person’s ability, character, or likely outcome.
- Create the counterfactual. Replace only the identity term or attribute. Preserve grammar, names or other details where appropriate, and the rest of the task.
- Repeat across templates. A single phrasing can produce an idiosyncratic result. Use multiple equivalent templates and record them exactly.
- Run the same settings. Keep the model version, system prompt, context, temperature, top-p, output limit, and other settings fixed within each comparison. If sampling is involved, repeat across a documented seed policy.
- Compare relevant outcomes. Depending on access and task, measure probabilities, classification labels, refusal and evasive-answer rates, and sampled outputs. Keep these outcomes separate rather than collapsing them into one score.
- Estimate uncertainty. Report effect sizes and confidence intervals, including the number of prompt variants and runs contributing to each estimate.
Probabilities and sampled text answer different questions. Probability comparisons can reveal preference among candidate continuations under a specified scoring setup; sampled outputs show what a user may receive under a decoding setup. A conclusion based on one should not be generalized to the other without evidence. Refusal and evasive-answer rates also deserve separate reporting: a system can avoid a substantive answer without producing an overtly stereotyped statement.
Which benchmarks help test task behavior?
Benchmarks can supply structured tasks and comparison conditions, but each covers a particular slice of bias. Use them to complement a use-case audit, not as a universal ranking of models.
| Benchmark | What it tests | Useful interpretation | Scope to keep in view |
|---|---|---|---|
| StereoSet | Stereotype versus anti-stereotype continuations across gender, profession, race, and religion. | Can show whether the model favors stereotypical continuations under the benchmark’s setup. | It is a continuation task, not a complete assessment of downstream decisions or open-ended generation. |
| BBQ | Question answering across nine U.S.-English social dimensions, using both ambiguous and informative contexts. | The contrast between ambiguous and informative contexts helps examine whether contextual evidence changes answers and whether bias affects accuracy. | Its stated coverage is nine dimensions and U.S. English; results should not be presented as coverage of all identities or languages. |
| LTF-TEST | Long-form generation over 14 topics and 10 demographic axes, with 11,948 samples in the study by Jeung et al. (2025). | Essay-style prompts can expose subtle effects that short answers may not reveal. | The published sample count and topic and axis coverage describe that study, not a required sample size for every audit. |
| IssueBench | Issue-bias testing with 2.49 million realistic prompts drawn from 3,900 templates and 212 political issues in Röttger et al. (2025). | Offers a large prompt collection for examining responses across political issues and varied templates. | Its issue-bias focus is distinct from a complete demographic-bias audit. |
BBQ’s dataset description reports that accuracy was up to 3.4 percentage points higher when the correct answer aligned with a social bias, widening to over 5 points for most gender examples. Those figures describe the benchmark findings, not a guaranteed effect for another model or a prediction about a deployment. Read performance together with the context type and answer choices; a higher aggregate accuracy alone can conceal which groups or cases bear the error.
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How should you evaluate generated text?
Generated text should be assessed with more than a single toxicity or stereotype score. Choose measures that match the harm being investigated and combine scalable scoring with human review.
Measure distinct outcomes separately
- Stereotype and representation: assess whether groups are associated with particular roles, traits, or narratives, and whether people are absent or narrowly represented.
- Toxicity: score hostile or harmful language, while recognizing that a score is a proxy and may not capture context or implication.
- Sentiment and regard: compare the tone and degree of respect or esteem directed toward different groups. A sentiment measure alone is not equivalent to regard.
- Parity and task outcomes: where the model performs a consequential classification or answer task, compare outcome rates and errors across groups. Examine false positives and false negatives rather than relying only on overall accuracy.
- Refusal and evasion: record when the system refuses or avoids answering, separate from the content of substantive responses.
For each metric, state the scoring method, what counts as a positive result, and how outputs were sampled. A scorer may itself be unreliable or biased, so an automated score should not be treated as ground truth. Have trained reviewers inspect a stratified sample, including examples flagged by scorers and examples not flagged, across relevant groups and prompt conditions. This helps surface both false positives and false negatives without relying only on conspicuous failures.
Keep the generation conditions attached to every reported result. Long responses, short completions, and task labels are different evidence. The LTF-TEST findings, for example, illustrate why essay-style prompts can reveal subtle effects missed by short answers; they do not establish that long-form tests replace shorter, task-specific checks.
How do you connect measurements to potential harm?
Triangulation means checking whether different methods point to a consistent concern, while being explicit when they do not. A representation association may be strong even when the tested task shows little disparity; a task disparity may appear without a simple embedding signal; and a scorer may flag text that reviewers judge harmless in context.
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Compare methods across construct validity, identity and language coverage, task realism, sensitivity to wording and decoding, statistical power, scorer reliability, reproducibility, and connection to plausible downstream harm. Intrinsic tests are comparatively inexpensive and scalable but distant from user outcomes. Task and generation tests are closer to behavior, but cost more and inherit the limitations of their scorers or annotators.
For each disparity, ask whether it persists across prompt variants and runs, whether it appears in more than one relevant outcome, which groups or situations account for it, and what the practical consequence would be in the intended use. Do not rank models from a single benchmark score. A score is most useful as a signal to investigate under a clearly defined setting, not as a universal quality label.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should an audit report include?
A useful report lets another team understand exactly what was tested and reproduce the comparison. Include:
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- Protected attributes, target constructs, language, intended use, and exclusions.
- All embedding word sets, sentence templates, prompt pairs, benchmark versions, and output-scoring instructions.
- Temperature, top-p, seed policy, decoding and output limits, and whether results use probabilities, samples, or both.
- Sample counts, effect sizes, confidence intervals, uncertainty method, and multiple-comparison handling.
- Separate results for classifications, false positives, false negatives, refusals, evasions, and generated-text measures where relevant.
- Scorer details, reviewer sampling and procedure, and examples that explain disagreements or important failure modes.
- Known scope limits, including language, identity coverage, prompt sensitivity, and how well the tests represent the intended deployment.
Preserve enough artifacts to rerun the audit: exact prompts, benchmark data or version references, analysis code, and configuration. If a model or API changes, treat the new version as a new condition rather than assuming that prior results carry forward. Present both supporting and conflicting signals; a clean-looking summary should not erase uncertainty or subgroup differences.
How do you turn results into an action?
Prioritize follow-up by the combination of the observed disparity, confidence in the measurement, and relevance to the system’s intended use. A noisy result from a distant proxy calls for replication and better task linkage; a persistent disparity in a consequential task warrants focused investigation even if an embedding test is unremarkable.
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- Replicate unstable findings. Re-run with documented seeds and prompt variants, and check whether results depend on one template, scorer, or identity term.
- Inspect error types. Review false positives, false negatives, refusals, evasions, and generated examples separately. Determine which group and context are affected.
- Test closer to use. Build task-grounded prompts reflecting the intended workflow while preserving controlled counterfactual comparisons.
- Document residual uncertainty. State what the audit supports, what it does not establish, and which languages, groups, or conditions were not assessed.
- Re-test after changes. A change to model version, prompt, decoding, scorer, or application context can alter behavior; retain the old configuration as a baseline for comparison.
The aim is not to prove a model unbiased. It is to make claims proportional to evidence, identify where the system behaves differently, and provide enough methodological detail for others to challenge, reproduce, and improve the findings.
Frequently Asked Questions
Can a high WEAT score prove an LLM discriminates?
No. It indicates an association in the tested representation and word sets, not how a deployed system will behave or whether a decision causes harm. Establishing a behavioral concern requires task and generation evidence tied to the use case.
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Why might a model’s bias score change when I rewrite a prompt?
Prompt wording is one of the conditions being measured: templates can frame identity and context differently, and decoding settings affect sampled responses. Report the prompt variants and generation configuration rather than treating one phrasing as definitive.
Does BBQ measure bias in languages other than English?
The benchmark described here covers nine social dimensions in U.S. English. A result on it does not establish behavior in another language, where wording, social categories, and task interpretation may differ.
What does a correlation between embedding and prompt-bias measures tell me?
It tells you how closely those particular measures varied in the study and aggregation used. It does not show that either measure predicts downstream harm, or that the same relationship holds for a different model or audit.
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