Coordinating layer-wise and sentence-wise early exits in LLMs produces multiplicative speedups of 1.4-2.3x over single-dimension early exit on sentiment classification tasks.
L ayer S kip: Enabling Early Exit Inference and Self-Speculative Decoding
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
roles
background 2polarities
background 2representative citing papers
CheckFree recovers intermediate stage failures in pipeline-parallel LLM training via neighbor averaging; CheckFree+ adds out-of-order execution to handle first/last stages by copying neighbors, with small embedding storage, outperforming checkpointing and redundancy at 5-10% failure rates by up to
DABS is a single-pass framework that builds a depth-ordered substrate from one Transformer encoding and performs lightweight aspect-conditioned readout, cutting computation by up to 60% on multi-aspect ATSA benchmarks while matching prior accuracy.
Looped MoE models scale better than standard transformers because different experts activate on each loop pass, recovering expressivity without extra parameters, and support superior early exits.
ConfLayers dynamically skips LLM layers based on confidence scores to create adaptive draft models for self-speculative decoding, reporting up to 1.4x speedup over standard generation.
Parcae stabilizes looped LLMs via spectral norm constraints on injection parameters, enabling power-law scaling for training FLOPs and saturating exponential scaling at test time that improves quality over fixed-depth baselines under fixed parameter budgets.
The survey organizes mechanistic interpretability techniques into a Locate-Steer-Improve framework to enable actionable improvements in LLM alignment, capability, and efficiency.
citing papers explorer
-
Two-dimensional early exit optimisation of LLM inference
Coordinating layer-wise and sentence-wise early exits in LLMs produces multiplicative speedups of 1.4-2.3x over single-dimension early exit on sentiment classification tasks.
-
All is Not Lost: LLM Recovery without Checkpoints
CheckFree recovers intermediate stage failures in pipeline-parallel LLM training via neighbor averaging; CheckFree+ adds out-of-order execution to handle first/last stages by copying neighbors, with small embedding storage, outperforming checkpointing and redundancy at 5-10% failure rates by up to
-
Single-Pass, Depth-Selective Reading for Multi-Aspect Sentiment Analysis
DABS is a single-pass framework that builds a depth-ordered substrate from one Transformer encoding and performs lightweight aspect-conditioned readout, cutting computation by up to 60% on multi-aspect ATSA benchmarks while matching prior accuracy.
-
Sparse Layers are Critical to Scaling Looped Language Models
Looped MoE models scale better than standard transformers because different experts activate on each loop pass, recovering expressivity without extra parameters, and support superior early exits.
-
ConfLayers: Adaptive Confidence-based Layer Skipping for Self-Speculative Decoding
ConfLayers dynamically skips LLM layers based on confidence scores to create adaptive draft models for self-speculative decoding, reporting up to 1.4x speedup over standard generation.
-
Parcae: Scaling Laws For Stable Looped Language Models
Parcae stabilizes looped LLMs via spectral norm constraints on injection parameters, enabling power-law scaling for training FLOPs and saturating exponential scaling at test time that improves quality over fixed-depth baselines under fixed parameter budgets.
-
Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models
The survey organizes mechanistic interpretability techniques into a Locate-Steer-Improve framework to enable actionable improvements in LLM alignment, capability, and efficiency.