Gated SAEs decouple which features to use from how large their activations should be, applying the L1 penalty only to selection and thereby eliminating shrinkage while halving the number of firing features needed for good fidelity.
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations , year =
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Introduces a new English dataset from r/AskParents and r/needadvice annotated for advice sentences plus preliminary models showing pre-trained LMs outperform rule-based systems but the task remains challenging.
A systematic literature review of explainability in multimodal attention models finds most studies focus on vision-language tasks with attention-based explanations, but evaluation methods lack consistency and modality-specific considerations.
citing papers explorer
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Improving Dictionary Learning with Gated Sparse Autoencoders
Gated SAEs decouple which features to use from how large their activations should be, applying the L1 penalty only to selection and thereby eliminating shrinkage while halving the number of firing features needed for good fidelity.
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Help! Need Advice on Identifying Advice
Introduces a new English dataset from r/AskParents and r/needadvice annotated for advice sentences plus preliminary models showing pre-trained LMs outperform rule-based systems but the task remains challenging.
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Decoding the Multimodal Maze: A Systematic Review on the Adoption of Explainability in Multimodal Attention-based Models
A systematic literature review of explainability in multimodal attention models finds most studies focus on vision-language tasks with attention-based explanations, but evaluation methods lack consistency and modality-specific considerations.