Papers with SST2
DoLFIn: Distributions over Latent Features for Interpretability (2020.coling-main)
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| Challenge: | Existing approaches to interpret neural networks face a trade-off between a model's usefulness and its complexity. |
| Approach: | They propose a novel approach to achieve interpretability that avoids this trade-off by using probability as the central quantity instead of a fixed quantity. |
| Outcome: | The proposed approach outperforms the classical CNN and BiLSTM classifiers on the SST2 and AG-news datasets. |
Rather a Nurse than a Physician - Contrastive Explanations under Investigation (2023.emnlp-main)
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| Challenge: | a recent study suggests that contrastive explanations are closer to how humans explain a decision than non-contrastive explanations. |
| Approach: | They analyze four English text-classification datasets to determine whether humans explain in contrast to alternatives. |
| Outcome: | The proposed explanations are closer to how humans explain a decision than non-contrastive explanations. |
OASIS: Mitigating Harmful Fine-tuning Attacks on LLMs via Orthogonal and Adaptive Safety Alignment Strategy (2026.acl-long)
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| Challenge: | Existing methods to decouple safety enforcement from harmful feature acquisition rely on perturbation directions that conflict with harmful gradients . harmful fine-tuning attacks pose a significant challenge for service providers aiming to uphold rigorous safety standards. |
| Approach: | They propose an orthogonal and ad hoc safety alignment strategy to decouple safety enforcement from harmful feature acquisition. |
| Outcome: | Experiments on four large language models show that OASIS reduces the Harmful Score by 60% compared to baselines while maintaining stable task utility. |