SLASH is a plug-and-play attention redistribution technique that counters attention sinks to enhance LLMs' intrinsic graph topology reconstruction without any training or fine-tuning.
The Thirty-ninth Annual Conference on Neural Information Processing Systems , year=
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A hypernetwork generates meta-gating parameters for SwiGLU blocks to let LLMs adapt their nonlinearity to arbitrary textual conditions, outperforming finetuning and meta-learning baselines with reasonable generalization to unseen cases.
LayerBoost selectively replaces or removes attention in non-critical transformer layers to cut inference latency up to 68% while recovering quality via brief distillation.
citing papers explorer
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SLASH the Sink: Sharpening Structural Attention Inside LLMs
SLASH is a plug-and-play attention redistribution technique that counters attention sinks to enhance LLMs' intrinsic graph topology reconstruction without any training or fine-tuning.
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Learn-to-learn on Arbitrary Textual Conditioning: A Hypernetwork-Driven Meta-Gated LLM
A hypernetwork generates meta-gating parameters for SwiGLU blocks to let LLMs adapt their nonlinearity to arbitrary textual conditions, outperforming finetuning and meta-learning baselines with reasonable generalization to unseen cases.
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LayerBoost: Layer-Aware Attention Reduction for Efficient LLMs
LayerBoost selectively replaces or removes attention in non-critical transformer layers to cut inference latency up to 68% while recovering quality via brief distillation.