Papers by Yacine Gaci
BTC-SAM: Leveraging LLMs for Generation of Bias Test Cases for Sentiment Analysis Models (2025.emnlp-main)
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Zsolt T. Kardkovács, Lynda Djennane, Anna Field, Boualem Benatallah, Yacine Gaci, Fabio Casati, Walid Gaaloul
| Challenge: | Sentiment Analysis (SA) models harbor inherent social biases that can be harmful in real-world applications. |
| Approach: | They propose a bias testing framework that generates high-quality test cases using Large Language Models (LLMs) for the controllable generation of test sentences. |
| Outcome: | The proposed framework generates high-quality test cases for bias testing in SA models with minimal specification using Large Language Models (LLMs) for the controllable generation of test sentences. |
Debiasing Pretrained Text Encoders by Paying Attention to Paying Attention (2022.emnlp-main)
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| Challenge: | Recent research has exposed text encoders for replicating discriminatory social biases which may cause unintended and undesired model behaviors with respect to social groups. |
| Approach: | They propose a method to reduce social stereotypes by redistributing attention scores of a text encoder so it forgets any preference to historically advantaged groups and attends to all social classes with the same intensity. |
| Outcome: | The proposed method reduces stereotypes and inflicts no semantic damage on pre-trained encoders. |
Conceptual Similarity for Subjective Tags (2022.findings-aacl)
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| Challenge: | Existing methods of matching user queries with subjective tags rely on cosine similarity or semantic similarity models fail to recognize conceptual connections between tags. |
| Approach: | They propose a conceptual similarity pipeline to leverage conceptual awareness when assessing similarity between tags. |
| Outcome: | The proposed pipeline generates high-quality datasets and evaluates the model on a downstream application. |