Einblick - Research - # Multilingual Stereotype Dataset

SeeGULL Multilingual: A Dataset of Global Stereotypes

Q: How can multilingual stereotype datasets like SeeGULL impact AI applications globally?

Multilingual stereotype datasets like SeeGULL can have a significant impact on AI applications globally by improving the safety and fairness of generative multilingual models. These datasets provide a diverse range of stereotypes across different languages and regions, allowing for more comprehensive evaluations of model performance. By incorporating geo-cultural factors into model evaluations, these datasets help in identifying and addressing biases that may be present in the output generated by AI systems. This leads to more culturally sensitive and inclusive AI applications that better reflect the diversity of global populations.

Q: What are potential drawbacks or criticisms of using stereotypical datasets in model evaluations?

One potential drawback of using stereotypical datasets in model evaluations is the risk of reinforcing harmful stereotypes rather than mitigating them. If not used carefully, these datasets could inadvertently perpetuate bias and discrimination in AI systems by amplifying negative societal perceptions. Additionally, there may be challenges in accurately capturing the complexity and nuances of stereotypes across different cultures, leading to oversimplification or misrepresentation. Critics may argue that relying solely on stereotypical datasets for evaluation purposes could limit the scope of analysis to predefined categories, overlooking emerging forms of bias or subtle variations in stereotypes. Furthermore, there is a concern about how these stereotypes are defined and annotated, as subjective interpretations by annotators could introduce their own biases into the dataset.

Q: How might cultural nuances influence the perception of offensive stereotypes across different regions?

Cultural nuances play a crucial role in shaping how offensive stereotypes are perceived across different regions. What may be considered offensive or derogatory in one culture could be viewed differently or even accepted as normal within another cultural context. These differences stem from varying historical backgrounds, social norms, values, beliefs, and power dynamics unique to each region. For example: In some cultures, certain attributes associated with gender roles or ethnicities may carry positive connotations while being seen as offensive elsewhere. The sensitivity towards specific topics such as religion, race, ethnicity varies greatly among cultures. Humor styles differ significantly between cultures which can affect what is deemed acceptable or inappropriate when it comes to stereotyping. Historical events or political climates can also influence how certain groups are portrayed through stereotypes within a particular region. Therefore understanding these cultural nuances is essential when evaluating offensive stereotypes as they shape perceptions and responses towards potentially harmful representations within society.

Kernkonzepte

The author introduces SeeGULL Multilingual, a dataset of global stereotypes, to address the lack of cross-cultural considerations in generative multilingual models' safety and fairness evaluations.

Zusammenfassung

The content discusses the creation of SeeGULL Multilingual, a dataset containing over 25K stereotypes across 20 languages and 23 regions. It highlights the importance of multilingual and multicultural model evaluations to prevent harmful stereotypes from propagating.

The dataset creation methodology involves identifying identity terms, generating associations using a language model, and obtaining culturally situated human annotations. The data showcases offensive stereotypes associated with different countries and regions.

Furthermore, the content evaluates foundation models like PaLM 2, GPT-4 Turbo, Gemini Pro, and Mixtral 8X7B on their endorsement of stereotypes present in SeeGULL Multilingual. The results emphasize the need for multilingual evaluation methods enabled by such datasets.

Overall, the work aims to improve model safeguards by providing a comprehensive stereotype resource with global coverage while acknowledging limitations and ethical considerations.

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Statistiken

Over 25K stereotypes across 20 languages.
Total of 25,861 stereotypes about 1,190 unique identities.
Offensive annotations collected for each stereotype.
Overlapping with English SeeGULL: about 5% unique stereotypes overlap.
Top countries with offensive stereotypes: Albania (2.09), Rwanda (1.99).
Countries with least offensive stereotypes: Singapore (-0.94), Canada (-0.91).

Zitate

"Stereotypes shared in this paper can be offensive." - Content Warning

Wichtige Erkenntnisse aus

SeeGULL Multilingual

by Mukul Bhutan... um arxiv.org 03-12-2024

https://arxiv.org/pdf/2403.05696.pdf

Tiefere Fragen

How can multilingual stereotype datasets like SeeGULL impact AI applications globally?

Multilingual stereotype datasets like SeeGULL can have a significant impact on AI applications globally by improving the safety and fairness of generative multilingual models. These datasets provide a diverse range of stereotypes across different languages and regions, allowing for more comprehensive evaluations of model performance. By incorporating geo-cultural factors into model evaluations, these datasets help in identifying and addressing biases that may be present in the output generated by AI systems. This leads to more culturally sensitive and inclusive AI applications that better reflect the diversity of global populations.

What are potential drawbacks or criticisms of using stereotypical datasets in model evaluations?

One potential drawback of using stereotypical datasets in model evaluations is the risk of reinforcing harmful stereotypes rather than mitigating them. If not used carefully, these datasets could inadvertently perpetuate bias and discrimination in AI systems by amplifying negative societal perceptions. Additionally, there may be challenges in accurately capturing the complexity and nuances of stereotypes across different cultures, leading to oversimplification or misrepresentation.
Critics may argue that relying solely on stereotypical datasets for evaluation purposes could limit the scope of analysis to predefined categories, overlooking emerging forms of bias or subtle variations in stereotypes. Furthermore, there is a concern about how these stereotypes are defined and annotated, as subjective interpretations by annotators could introduce their own biases into the dataset.

How might cultural nuances influence the perception of offensive stereotypes across different regions?

Cultural nuances play a crucial role in shaping how offensive stereotypes are perceived across different regions. What may be considered offensive or derogatory in one culture could be viewed differently or even accepted as normal within another cultural context. These differences stem from varying historical backgrounds, social norms, values, beliefs, and power dynamics unique to each region.
For example:

In some cultures, certain attributes associated with gender roles or ethnicities may carry positive connotations while being seen as offensive elsewhere.
The sensitivity towards specific topics such as religion, race, ethnicity varies greatly among cultures.
Humor styles differ significantly between cultures which can affect what is deemed acceptable or inappropriate when it comes to stereotyping.
Historical events or political climates can also influence how certain groups are portrayed through stereotypes within a particular region.
Therefore understanding these cultural nuances is essential when evaluating offensive stereotypes as they shape perceptions and responses towards potentially harmful representations within society.