HARECast stabilizes cross-sample variance in attention-response energy via group-wise regularization to reduce prediction errors in precipitation nowcasting.
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HETA is a new attribution framework for decoder-only LLMs that combines semantic transition vectors, Hessian-based sensitivity scores, and KL divergence to produce more faithful and human-aligned token attributions than prior methods.
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Stable Attention Response for Reliable Precipitation Nowcasting
HARECast stabilizes cross-sample variance in attention-response energy via group-wise regularization to reduce prediction errors in precipitation nowcasting.
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Hessian-Enhanced Token Attribution (HETA): Interpreting Autoregressive LLMs
HETA is a new attribution framework for decoder-only LLMs that combines semantic transition vectors, Hessian-based sensitivity scores, and KL divergence to produce more faithful and human-aligned token attributions than prior methods.