Title: SeeGULL: A Stereotype Benchmark with Broad Geo-Cultural Coverage Leveraging Generative Models
Benchmark: SeeGULL (Stereotypes Generated Using LLMs in the Loop)
Summary: This paper introduces SeeGULL, a broad-coverage dataset of stereotypes spanning 178 countries across six continents. SeeGULL is built using the generative capabilities of LMs, and it also includes offensiveness scores for the stereotypes as well as human annotations.
Phenomenon type: General form of bias
Target phenomenon: They want to measure social stereotypes in LMs.
Phenomenon defined by authors: Yes
Phenomenon definition: "Stereotypes are generalized beliefs about categories of people, and are often reflected in data as statistical associations, which the language models rely on to associate concepts." (p. 9851)
Definition scope: Subset
Definition integrity: Composite phenomenon
Definition integrity detail: Yes
Phenomenon contested: Contested
Task definition: SeeGULL consists of (identity, attribute) tuples such as (Italian, gangsters) as well as metadata. The paper does not present a task per se; rather, SeeGULL forms a basis on which different tasks/evaluations can be performed.
Task item definition: Each item consists of an (identity, attribute) tuple such as (Italian, gangsters), annotations from three raters indicating stereotypicality, and an offensiveness score.
Task source: Crowd-sourced task examples (e.g. Prolific-created tasks), Procedurally-generated task examples (e.g. Creating instances from a template), LLM-generated task examples (e.g. Filtered from responses to a prompt)
Task ecology: Constructed task (e.g. predicting medical diagnoses from clinicians' notes)
Dataset size: 7,750
Dataset metadata available: Yes
Dataset metadata detail: Each item is accompanied by annotations from three raters indicating stereotypicality and an offensiveness score.
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: Multiple choice
Response format detail: The authors only collect the dataset, without specifying a task. In their experiments, they apply the framework proposed by Dev at al. (2020), which measures bias using a natural language inference setup.
Metric definition: Mean entailment
Metric definition detail: Mean entailment on the natural language inference task is meant to measure strereotype strength.
Metric aggregation: Simple Mean
Metric statistics: simple mean
Metric subscores: Yes
Metric subscores detail: different regions
Metric access: Outputs alone
Few-shot evaluation: No
Authorship: Mix (multiple authors from industry and academia)
Benchmark availability: Yes
Task face validity: Yes
Metric face validity: Yes
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: They conduct a human validation study.
Taxonomy: Alignment / Bias
New taxonomy: General Capability / Bias