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Found 8 papers, 3 papers with code
GATE X-E : A Challenge Set for Gender-Fair Translations from Weakly-Gendered Languages
Neural Machine Translation (NMT) continues to improve in quality and adoption, yet the inadvertent perpetuation of gender bias remains a significant concern.
Reducing Gender Bias in Machine Translation through Counterfactual Data Generation
By using this data to fine-tune an existing NMT model, they show that gender bias can be significantly mitigated, albeit at the expense of translation quality due to catastrophic forgetting.
Evaluating Gender Bias in the Translation of Gender-Neutral Languages into English
To address this gap, we introduce GATE X-E, an extension to the GATE (Rarrick et al., 2023) corpus, that consists of human translations from Turkish, Hungarian, Finnish, and Persian into English.
KITAB: Evaluating LLMs on Constraint Satisfaction for Information Retrieval
Motivated by rising concerns around factual incorrectness and hallucinations of LLMs, we present KITAB, a new dataset for measuring constraint satisfaction abilities of language models.
Diversity of Thought Improves Reasoning Abilities of LLMs
Large language models (LLMs) are documented to struggle in settings that require complex reasoning.
Attention Satisfies: A Constraint-Satisfaction Lens on Factual Errors of Language Models
We investigate the internal behavior of Transformer-based Large Language Models (LLMs) when they generate factually incorrect text.
Social Biases through the Text-to-Image Generation Lens
In this paper, we take a multi-dimensional approach to studying and quantifying common social biases as reflected in the generated images, by focusing on how occupations, personality traits, and everyday situations are depicted across representations of (perceived) gender, age, race, and geographical location.
GATE: A Challenge Set for Gender-Ambiguous Translation Examples
Although recent years have brought significant progress in improving translation of unambiguously gendered sentences, translation of ambiguously gendered input remains relatively unexplored.
