SimLens for Early Exit in Large Language Models: Eliciting Accurate Latent Predictions with One More Token

arXiv:2507.17618 · cs.CL, cs.PF · Submitted 2025-07-23 · Read on arXiv

cs.CL, cs.PF

Submitted: 2025-07-23

Updated: 2026-08-26

Terminology

• Accuracy — 63× in this paper · explained in 1 episode
• Ablation — 8× in this paper · explained in 1 episode
• Decode — 8× in this paper · explained in 2 episodes
• Alignment — 7× in this paper · explained in 1 episode
• Transformer — 7× in this paper · explained in 2 episodes
• Distillation — 5× in this paper · explained in 2 episodes
• Generalization — 5× in this paper · explained in 13 episodes
• Perplexity — 4× in this paper · explained in 3 episodes
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Large Language Models (LLMs) — 3× in this paper · explained in 6 episodes
• Linear probe — 3× in this paper · explained in 2 episodes
• Quantization — 3× in this paper · explained in 12 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Core — 2× in this paper · explained in 2 episodes
• In-Context Learning — 2× in this paper · explained in 4 episodes
• Knowledge Distillation — 2× in this paper · explained in 11 episodes
• Pruning — 2× in this paper · explained in 4 episodes
• Speculative Decoding — 2× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Accuracy Alignment — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Sparsity — 1× in this paper · explained in 4 episodes

Sources

• Analyzing Transformers in Embedding Space
• Performance-Guided LLM Knowledge Distillation for Efficient Text Classification at Scale
• Jump to Conclusions: Short-Cutting Transformers With Linear Transformations
• Not All Layers of LLMs Are Necessary During Inference
• MaskLLM: Learnable Semi-Structured Sparsity for Large Language Models
• Dissecting Recall of Factual Associations in Auto-Regressive Language Models
• Why do LLMs attend to the first token?
• Patchscopes: A Unifying Framework for Inspecting Hidden Representations of Language Models
• Eliciting Latent Predictions from Transformers with the Tuned Lens
• BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions
• Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge
• When Attention Sink Emerges in Language Models: An Empirical View
• Overthinking the Truth: Understanding how Language Models Process False Demonstrations
• Optimizing Large Language Models through Quantization: A Comparative Analysis of PTQ and QAT Techniques
• Confident Adaptive Language Modeling
• In-Context Learning Creates Task Vectors
• Does Representation Matter? Exploring Intermediate Layers in Large Language Models
• Layer by Layer: Uncovering Hidden Representations in Language Models
• I-LLM: Efficient Integer-Only Inference for Fully-Quantized Low-Bit Large Language Models
• Accelerating Large Language Model Inference with Self-Supervised Early Exits

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