BBQ: A Hand-Built Bias Benchmark for Question Answering (2110.08193v2)

Abstract: It is well documented that NLP models learn social biases, but little work has been done on how these biases manifest in model outputs for applied tasks like question answering (QA). We introduce the Bias Benchmark for QA (BBQ), a dataset of question sets constructed by the authors that highlight attested social biases against people belonging to protected classes along nine social dimensions relevant for U.S. English-speaking contexts. Our task evaluates model responses at two levels: (i) given an under-informative context, we test how strongly responses reflect social biases, and (ii) given an adequately informative context, we test whether the model's biases override a correct answer choice. We find that models often rely on stereotypes when the context is under-informative, meaning the model's outputs consistently reproduce harmful biases in this setting. Though models are more accurate when the context provides an informative answer, they still rely on stereotypes and average up to 3.4 percentage points higher accuracy when the correct answer aligns with a social bias than when it conflicts, with this difference widening to over 5 points on examples targeting gender for most models tested.

• The paper introduces BBQ, a hand-built dataset designed to identify and measure social biases in QA models across multiple identity attributes.
• The paper shows that models, including UnifiedQA, often default to stereotype-aligned answers in ambiguous contexts and suffer up to a 5% accuracy drop in disambiguated gender-bias cases.
• The paper emphasizes the need for improved debiasing methods to prevent representational harms in real-world AI applications.

Overview of "BBQ: A Hand-Built Bias Benchmark for Question Answering"

The paper entitled "BBQ: A Hand-Built Bias Benchmark for Question Answering" focuses on the issue of social biases manifesting within the outputs of LLMs (LMs) used in question answering (QA) tasks. The authors introduce the Bias Benchmark for QA (BBQ), a benchmark specifically designed to evaluate social biases in QA models across nine social dimensions pertinent to U.S. English-speaking contexts. This research is situated within a broader interest in understanding how social biases imbued within LMs can lead to representational harms when applied in real-world contexts.

Dataset and Methodology

BBQ is a hand-crafted dataset comprised of question sets targeting biases pertinent to identity attributes such as age, disability, gender, race/ethnicity, and more. The dataset features both ambiguous contexts, where the model does not have sufficient information to make an informed judgment, and disambiguated contexts, where the answer can be inferred from the context provided. The authors conducted comprehensive validations to ensure that the templates used in BBQ accurately reflect real-world biases and that human annotators agree with the designated correct answers.

The dataset covers a broad expanse of possible biases and allows for the investigation of how prominent models—namely UnifiedQA, RoBERTa, and DeBERTaV3—perform when stereotype reinforcement could potentially override correct judgments. The hand-crafted nature allows for precise targeting of known biases, providing a rigorous metric for evaluating how such biases influence model outputs.

Key Findings

The paper reveals patterns in model behaviors that indicate reliance on social biases in both ambiguous and disambiguated contexts. In ambiguous contexts, the models frequently default to answers that align with known social stereotypes instead of indicating uncertainty, which would be the correct response when information is lacking. UnifiedQA, particularly, shows a high incidence of bias alignment, with a reinforcement of stereotypes noticeable in error rates where accurate responses should translate to "unknown."

In disambiguated contexts, while accuracy generally improves across all tested models, the accuracy when a model's stereotyped biases conflict with the contextually correct response decreases significantly, with a drop of up to 5 percentage points reported in gender-bias instances.

Implications and Future Directions

The implications of these findings are substantial for the deployment of QA systems using LMs. The persistence of stereotype-driven errors suggests a potential for exacerbating bias in applied settings—particularly concerning as these models see increased use across various sensitive domains, such as automated customer support and educational tools.

This paper underlines the necessity of further research on debiasing methods and the importance of integrating variability to reflect a more accurate and less harmful model output. The authors position BBQ not as a conclusive solution but as a means to enhance discussions around bias detection and modeling. BBQ's detailed exploration of biases across an extensive array of identity attributes provides a foundational tool that can spur future research into effectively mitigating these biases across multiple contexts.

Conclusion

The BBQ benchmark provides a critical lens to examine the susceptibility of QA models to reflect and propagate societal biases. By presenting detailed methodologies and results, this paper invites further exploration into the multifaceted challenges posed by bias in LLMs and emphasizes the urgency of addressing these issues in a framework that acknowledges societal impact. The paper's insights are vital for researchers engaged in developing more equitable AI systems.