Papers by Shuhan Yuan
Discovering and Mitigating Indirect Bias in Attention-Based Model Explanations (2024.findings-naacl)
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| Challenge: | Discrimination is the unfair treatment or prejudice directed towards individuals, groups, or certain ideas or beliefs, intentionally or unintentionally. |
| Approach: | They propose an algorithm to detect and mitigate indirect bias in transformer models by leveraging attention explanations. |
| Outcome: | The proposed algorithm shows that it is more accurate than traditional fairness metrics and that it can be used to mitigate bias in transformer models. |
Robust Hate Speech Detection via Mitigating Spurious Correlations (2022.aacl-short)
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| Challenge: | a novel hate speech detection model can be used to detect word- and character-level adversarial attacks . existing adversarials assume that attackers replace the target words with other names to evade detection . |
| Approach: | They propose a robust hate speech detection model that can defend against adversarial attacks . they describe the process of hate speech recognition by a causal graph and a regularized entropy loss function to quantify spurious correlation . |
| Outcome: | The proposed model can defend against word- and character-level adversarial attacks. |
Fine-tuning LLMs with Cross-Attention-based Weight Decay for Bias Mitigation (2025.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) excel in natural language processing tasks but often propagate societal biases from their training data, leading to discriminatory outputs. |
| Approach: | They propose a method that modifies the LLM architecture to mitigate bias by adjusting the attention weights of sensitive tokens. |
| Outcome: | The proposed method can handle multiple sensitive attributes and does not require full knowledge of sensitive tokens presented in the dataset. |
Generating Textual Adversaries with Minimal Perturbation (2022.findings-emnlp)
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| Challenge: | Existing word-level adversarial approaches for textual data have various limitations due to the large search space consisting of combinations of candidate words. |
| Approach: | They propose a novel attack strategy to find adversarial texts with high similarity to original texts without perturbation. |
| Outcome: | The proposed approach achieves higher success rates and lower perturbation rates in four benchmark datasets compared with state-of-the-art approaches. |