Papers by Camilla Casula
Job Unfair: An Investigation of Gender and Occupational Bias in Free-Form Text Completions by LLMs (2025.emnlp-main)
Copied to clipboard
| Challenge: | a recent study has identified that LLMs are used in domains where they support or replace human decision-making . a systematic review of LLM outputs shows that many facets of social bias remain unaccounted for . |
| Approach: | They propose to disentangle gender and occupational biases in Italian and English as expressed by LLMs. |
| Outcome: | The proposed method captures gender and occupational biases in Italian and English . it also shows that models struggle with gender-neutral expressions, especially beyond English - the authors conclude . |
Generation-Based Data Augmentation for Offensive Language Detection: Is It Worth It? (2023.eacl-main)
Copied to clipboard
| Challenge: | generative data augmentation has been shown to be effective in offensive language detection but the potential for bias injection has not been investigated. |
| Approach: | They propose to investigate the robustness of models trained on generated data in a variety of data augmentation setups and analyze models using the HateCheck suite. |
| Outcome: | The proposed model training setups on four English offensive language datasets are robust and robust, while the generative DA setups do not present bias injection issues. |
Real Men are Tough: Evaluating Gender Bias and Sensitivity to Masculinity Norms in LLMs (2026.findings-acl)
Copied to clipboard
| Challenge: | Large language models exhibit gender bias, but most evaluations focus on downstream stereotypes . a recent study found that explicit endorsement of masculinity norms is low across models . |
| Approach: | They investigate whether large language models rely on traditional masculinity norms as latent priors in gender-biased inference. |
| Outcome: | The findings show that large language models rely on stereotypes as latent priors . the authors used the Male Role Norms Inventory (MRNI) to investigate gender bias . |
Don’t Augment, Rewrite? Assessing Abusive Language Detection with Synthetic Data (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing datasets for abusive language detection and content moderation are limited by regulatory bodies and social media platforms. |
| Approach: | They propose to replace existing datasets in English with synthetic data by rewriting original texts with an instruction-based generative model. |
| Outcome: | The proposed model improves performance in cross-dataset training. |
Variationist: Exploring Multifaceted Variation and Bias in Written Language Data (2024.acl-demos)
Copied to clipboard
| Challenge: | Existing tools that inspect and visualize language data are limited in their capabilities. |
| Approach: | They propose a highly-modular, extensible, and task-agnostic tool that inspects language variation and bias across multiple variables, language units, and diverse metrics. |
| Outcome: | The proposed tool can inspect and visualize language variation and bias across variables, language units, and diverse metrics that go beyond descriptive statistics. |
Delving into Qualitative Implications of Synthetic Data for Hate Speech Detection (2024.emnlp-main)
Copied to clipboard
| Challenge: | Recent work on synthetic data for training models for NLP tasks reports mixed results on subjective tasks such as hate speech detection. |
| Approach: | They propose to use synthetic data to train models for highly subjective tasks such as hate speech detection to investigate the potential and specific pitfalls of using it. |
| Outcome: | The proposed model outperforms models trained with real data on hate speech detection tasks, but it fails to accurately reflect real-world data on linguistic dimensions and results in different class distributions. |