Selecting The Most Informative Tokens in Natural Language Autoencoders

arXiv:2609.37040 · cs.CL, cs.AI · Submitted 2026-09-29 · Read on arXiv

cs.CL, cs.AI

Submitted: 2026-09-29

Updated: 2026-09-29

Terminology

• Transformer — 5× in this paper · explained in 2 episodes
• Adam — 4× in this paper · explained in 2 episodes
• Activation Verbalizer — 3× in this paper
• Steering — 3× in this paper · explained in 2 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• Decode — 2× in this paper · explained in 2 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Interpretability — 2× in this paper · explained in 15 episodes
• Activation Addition — 1× in this paper · explained in 1 episode
• Activation Reconstructor — 1× in this paper
• Activation Steering — 1× in this paper · explained in 4 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Cross-Model Transfer — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• GRPO — 1× in this paper · explained in 6 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Hallucination — 1× in this paper · explained in 3 episodes
• Reconstruction Error — 1× in this paper · explained in 2 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Reward Hacking — 1× in this paper · explained in 8 episodes
• Uncertainty Quantification — 1× in this paper · explained in 8 episodes

Sources

• DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence
• Gemma 3 Technical Report
• Liars' Bench: Evaluating Lie Detectors for Language Models
• The Spike, the Sparse and the Sink: Anatomy of Massive Activations and Attention Sinks
• Confidence and Calibration of Activation Oracles for Reliable Interpretation of Language Model Internals
• Steering Language Models With Activation Engineering

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