Title: IndiBias: A Benchmark Dataset to Measure Social Biases in Language Models for Indian Context
Benchmark: IndiBias
Summary: This paper examines LMs' biases and stereotypes in the Indian context. Specifically, the authors introduce IndiBias, a benchmark consisting of an adapted, translated, and augmented version of CrowS-Pairs. The authors also conduct a variant of SEAT to measure intersectional biases in LMs. Using both their Indian variant of CrowS-Pairs and SEAT, the authors test a series of LMs, finding varying levels of social biases.
Phenomenon type: General form of bias
Target phenomenon: They want to measure "LM's biases and stereotypes in the Indian context" (p. 8786).
Phenomenon defined by authors: No
Phenomenon definition: The authors do not provide an explicit definition but talk about "preference for stereotypical associations in diverse social contexts," which can be seen as their definition of bias (p. 8786).
Definition scope: Comprehensive
Definition integrity: Composite phenomenon
Definition integrity detail: Yes
Phenomenon contested: Contested
Task definition: IndiBias consists of two parts: an Indian variant of CrowS-Pairs, where sentence pairs are probability-scored by LMs to measure bias, and a variant of SEAT that aims to measure intersectional biases. Only the first part fulfils the inclusion criteria of a benchmark, so this description will focus on it.
Task item definition: Each item is a sentence pair, where the first sentence contains an identity term and a stereotypical or anti-stereotypical association, and the second sentence contains a different identity term that makes the sentence more or less stereotypical compared to the first sentence.
Task source: Modified from another benchmark (e.g. translation into another language), 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: English portion: 800. Hindi portion (translation of English portion): 800.
Dataset metadata available: No
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: This task is not based on LM responses; it solely relies on measuring the probabilities assigned to tokens in the sentence pairs.
Metric definition: Bias Percentage: percentage of sentence pairs for which the more stereotypical sentence has a higher probability than the less stereotypical sentence.
Metric aggregation: Simple Mean
Metric statistics: simple mean
Metric subscores: Yes
Metric subscores detail: Seven social categories: gender, religion, caste, age, region, physical appearance, occupation/socioeconomic status
Metric access: Model access required (e.g. logits)
Few-shot evaluation: No
Authorship: Mix (multiple authors from industry and academia)
Benchmark availability: The link works, but the GitHub repo does only contain a sample of all 800 items.
Task face validity: Yes
Metric face validity: High values of the metric indicate presence of bias, but low values do not mean that a model is unbiased. The authors do not acknowledge that.
Results interpreted by authors: Yes
Results compared: No
Results comparison explanation: No comparisons made
Results realism: No
Human baseline: No
Authors discuss validity: No
Taxonomy: Alignment / Bias
New taxonomy: General Capability / Bias