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Found 10 papers, 4 papers with code
GeniL: A Multilingual Dataset on Generalizing Language
We argue that understanding the sentential context is crucial for detecting instances of generalization.
SeeGULL Multilingual: a Dataset of Geo-Culturally Situated Stereotypes
While generative multilingual models are rapidly being deployed, their safety and fairness evaluations are largely limited to resources collected in English.
ViSAGe: A Global-Scale Analysis of Visual Stereotypes in Text-to-Image Generation
First, we show that stereotypical attributes in ViSAGe are thrice as likely to be present in generated images of corresponding identities as compared to other attributes, and that the offensiveness of these depictions is especially higher for identities from Africa, South America, and South East Asia.
SeeGULL: A Stereotype Benchmark with Broad Geo-Cultural Coverage Leveraging Generative Models
Stereotype benchmark datasets are crucial to detect and mitigate social stereotypes about groups of people in NLP models.
PaLM 2 Technical Report
Through extensive evaluations on English and multilingual language, and reasoning tasks, we demonstrate that PaLM 2 has significantly improved quality on downstream tasks across different model sizes, while simultaneously exhibiting faster and more efficient inference compared to PaLM.
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Cultural Re-contextualization of Fairness Research in Language Technologies in India
Recent research has revealed undesirable biases in NLP data and models.
Bootstrapping Multilingual Semantic Parsers using Large Language Models
Despite cross-lingual generalization demonstrated by pre-trained multilingual models, the translate-train paradigm of transferring English datasets across multiple languages remains to be a key mechanism for training task-specific multilingual models.
Re-contextualizing Fairness in NLP: The Case of India
In this paper, we focus on NLP fair-ness in the context of India.
Parameter-Efficient Finetuning for Robust Continual Multilingual Learning
We address this challenge by proposing LAFT-URIEL, a parameter-efficient finetuning strategy which aims to increase the number of languages on which the model improves after an update, while reducing the magnitude of loss in performance for the remaining languages.
MuRIL: Multilingual Representations for Indian Languages
This can be explained by the fact that multilingual language models (LMs) are often trained on 100+ languages together, leading to a small representation of IN languages in their vocabulary and training data.
