Title: ROBBIE: Robust Bias Evaluation of Large Generative Language Models
Benchmark: AdvPromptSet, HolisticBiasR (ROBBIE, mentioned in the title, refers to a combination of several existing benchmarks with these two new benchmarks)
Summary: This paper attempts to make bias evaluation more robust by examining several bias benchmarks at the same time, two of which are new contributions of the paper, an approach referred to as ROBBIE (Robust Bias Evaluation). The authors apply ROBBIE to several LMs and also examine the effects of different bias mitigation strategies.
Phenomenon type: General form of bias
Target phenomenon: They want to measure social bias against different demographic groups as manifested in LM generations.
Phenomenon defined by authors: Yes
Phenomenon definition: "we define ``bias'' in this work as the proportion of subgroups for which the frequency of toxicity and negative regard generations falls outside an acceptable threshold"
Definition scope: Comprehensive
Definition integrity: Composite phenomenon
Definition integrity detail: Yes
Phenomenon contested: Contested
Task definition: The task is to generate continuations of prompts that contain mentions of demographic groups. The LMs should avoid toxic generations in response to these prompts (but see the limitations of the specific metric used to operationalize this below).
Task item definition: Each item in both AdvPromptSet and HolisticBiasR is a prompt that contains mentions of one or more demographic groups.
Task source: Modified from another benchmark (e.g. translation into another language), Procedurally-generated task examples (e.g. Creating instances from a template)
Task ecology: Representative task (e.g. answering medical licensing exam questions)
Dataset size: AdvPromptSet: 199,403. HolisticBiasR: 214,460.
Dataset metadata available: Yes
Dataset metadata detail: toxicity of prompt, identity labels for mentioned demographic group(s), number of toxicity and bias terms present in the prompt
Sampling method: Targeted items (creators defined a task space and chose tasks within it strategically), Specific criteria (items were taken from a larger set based on specified rules)
Split reviewed: Test
Response format: Free response (e.g. summary paragraph, executable code)
Metric definition: BiasScore: percentage of demographic groups in a dataset for which the LM continuations are more negative (e.g., toxic) than the average percentage of negative generations across demographic groups
Metric definition detail: BiasScore seems like an ill-defined metric. For example, if an LM outputs exclusively negative continuations, the BiasScore is zero.
Metric subscores: No
Metric access: Outputs alone
Few-shot evaluation: No
Authorship: Industry
Benchmark availability: Yes
Task face validity: Yes
Metric face validity: It works since most LMs do not output toxic content all the time, but this does not make it a metric that is suitable for bias measurement in principle.
Results interpreted by authors: Yes
Results compared: Yes
Results comparison explanation: Yes
Results realism: No
Human baseline: No
Authors discuss validity: Yes
Author validity detail: The authors use converging evidence across several benchmarks (and comparisons across benchmarks) as a way to increase construct validity.
Taxonomy: Alignment / Bias
New taxonomy: General Capability / Bias