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This paper audits multilingual clinical ASR systems on psychiatric interviews in Indian languages and proposes SamaVaani, a unified debiasing technique to improve performance and fairness across demographic groups.
A post-hoc method reduces spurious correlations in fine-tuned LLMs by truncating the tail of the SVD of the weight update matrix. It reduces the spurious-group gap by up to 5x with less than 2pp accuracy loss, without retraining or group labels.
PEARL introduces a contrastive percentile approximation framework to mitigate behavioral intensity imbalance in recommender systems, achieving significant gains in engagement metrics in a production livestream platform serving billions of users.
DebiasRAG proposes a tuning-free, query-specific debiasing framework using retrieval-augmented generation to reduce social biases in LLMs without degrading their original capabilities.
Researchers from MIT, WPI, and Google propose WRING, a novel post-processing debiasing method for Vision-Language Models that avoids the 'Whac-a-mole dilemma' of amplifying other biases when removing specific ones.
This paper proposes Product-of-Experts (PoE) training to reduce dataset artifacts in Natural Language Inference, downweighting examples where biased models are overconfident. PoE nearly preserves accuracy on SNLI (89.10% vs. 89.30%) while reducing bias reliance by ~4.85 percentage points.