Evaluating and Interpreting Gender Bias in LLM Feedback: Span-Level Embedding-Based Evidence from Automated Essay Feedback

Investigating How LLMs Propagate Female Stereotypes: Comparing What Models Say via Prompts with What They Represent in Their Embeddings

Linguistic Knowledge-Infused Fine-Tuning for Mitigating Gender Bias in Machine Translation

Large Language Models (LLMs) achieve strong performance in machine translation (MT) but often encode gender bias, particularly when translating from non-gendered into gendered languages. This paper introduces a fine-tuning strategy to mitigate such bias in English → Spanish and English → Catalan translation. Using para...

Rethinking Gender Annotation for Bias Evaluation in Machine Translation: Can LLMs Improve Reliability?

From Word to Prompt: Assessing the Linguistic Footprint of LLMs on Spanish-Language Bachelor’s and Master’s Theses

From Body to Mind: Analyzing Gender Representation in Spanish Generative Language Models

A rigorous, large-scale analysis of gender bias in a diverse set of 20 publicly available Spanish generative LLMs, finding systematic patterns consistent with pervasive cultural stereotypes, echoing findings from earlier masked language models are uncovered.

Acmite: Mitigating Gender Bias in LLMs through Concept-Guided Mutual Information

Large language models (LLMs) can reproduce social stereotypes from their training data, motivating extensive research on model debiasing. However, existing methods often rely on explicit biased examples or predefined group-term substitutions, making them sensitive to wording and less effective at capturing stereotype c...