What it measures

Ambiguous and disambiguated questions test answer accuracy and social bias across protected social categories. Accuracy, signed bias, and name-based variants answer different questions.

Reading the measurement

Separate satisfying instructions from factual accuracy, human preference and safe behavior. Inspect strict all-constraint success as well as average criterion credit. Judge choice and adversarial prompt selection can materially change the result.

Published evaluation

Author-released RoBERTa/DeBERTa predictions, joined to exact example IDs and gold labels. Signed bias follows the authors’ target_loc formula; zero is neutral. Identical scoring metadata is deduplicated so each prediction counts once. This is an aggregation of existing predictions, not a new model evaluation.

Where this fits in an evaluation suite

Format compliance, preference, truthfulness, and safety require separate measurements. Keep desirable writing style from masking factual errors.

Start with a capability hypothesis, then choose a second test with a different failure mode. Keep the checkpoint, reasoning effort, agent tools, sampling policy, and scoring procedure fixed when measuring a training intervention. Record failures and missing outputs in the denominator.

A reproducible comparison

Author-released RoBERTa/DeBERTa predictions, joined to exact example IDs and gold labels. Signed bias follows the authors’ target_loc formula; zero is neutral. Identical scoring metadata is deduplicated so each prediction counts once. This is an aggregation of existing predictions, not a new model evaluation.

Publish the exact run configuration, item coverage, score units, and available uncertainty. If a source supplies only an aggregate or a single run, preserve that limit. A new model name or a rounded score alone does not establish a capability improvement.

PRIMARY EVIDENCE & FURTHER READING

  1. BBQ: A Hand-Built Bias Benchmark for Question Answering ↗

    Parrish, Alicia, Chen, Angelica, Nangia, Nikita, Padmakumar, Vishakh, Phang, Jason, Thompson, Jana et al. · 2021

    All 8 authors

    Parrish, Alicia, Chen, Angelica, Nangia, Nikita, Padmakumar, Vishakh, Phang, Jason, Thompson, Jana, Htut, Phu Mon, Bowman, Samuel R.

  2. BBQ: A Hand-Built Bias Benchmark for Question Answering — released RoBERTa and DeBERTa predictions ↗

    Parrish, Alicia, Chen, Angelica, Nangia, Nikita, Padmakumar, Vishakh, Phang, Jason, Thompson, Jana et al. · 2021

    All 8 authors

    Parrish, Alicia, Chen, Angelica, Nangia, Nikita, Padmakumar, Vishakh, Phang, Jason, Thompson, Jana, Htut, Phu Mon, Bowman, Samuel R.

Reviewed 2026-10-27. Explanation and interpretation by RLTasks. Benchmark ownership remains with its original authors.