Attention is Case-Sensitive

arXiv:2608.03711 · cs.CV, cs.CL, cs.LG · Submitted 2026-08-04 · Read on arXiv

Maximilian Dillitzer, Tin Stribor Sohn, Jason J. Corso, Michael Auerbach

cs.CV, cs.CL, cs.LG

Submitted: 2026-08-04

Comments: Accepted at ECCV 2026

Code: https://github.com/openai/tiktoken

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

Accuracy — 61× in this paper · explained in 1 episode
Target Emphasis — 47× in this paper
Adversarial De-Emphasis — 43× in this paper
Steering — 43× in this paper · explained in 2 episodes
Benchmark — 36× in this paper · explained in 2 episodes
Target Title Case — 36× in this paper
Tokenization — 32× in this paper · explained in 2 episodes
Transformer — 18× in this paper · explained in 2 episodes
Robustness — 9× in this paper · explained in 6 episodes
Attention Maps — 8× in this paper · explained in 2 episodes
Coherence — 8× in this paper · explained in 2 episodes
Generalization — 7× in this paper · explained in 14 episodes
Transformers — 7× in this paper · explained in 2 episodes
Consistency — 5× in this paper · explained in 3 episodes
Core — 5× in this paper · explained in 2 episodes
Cosine Similarity — 5× in this paper · explained in 3 episodes
Embeddings — 5× in this paper · explained in 2 episodes
Grounding — 5× in this paper · explained in 3 episodes
Semantic Drift — 5× in this paper · explained in 3 episodes
Target Alternating Case — 5× in this paper
Vision-language models — 5× in this paper · explained in 2 episodes
Attention Mechanism — 4× in this paper · explained in 3 episodes
Transformer Architecture — 4× in this paper · explained in 4 episodes
Adam — 3× in this paper · explained in 3 episodes
Byte-Pair Encoding — 3× in this paper
Fine-Tuning — 3× in this paper · explained in 5 episodes
Language Models (LLMs) — 3× in this paper · explained in 2 episodes
Activation Steering — 2× in this paper · explained in 7 episodes
Computational overhead — 2× in this paper · explained in 2 episodes
Cross-Attention Maps — 2× in this paper · explained in 2 episodes
Interpretability — 2× in this paper · explained in 15 episodes
Large Language Models (LLMs) — 2× in this paper · explained in 9 episodes
Universality — 2× in this paper · explained in 2 episodes
Vision-Language Models (VLMs) — 2× in this paper · explained in 7 episodes
Visual Grounding — 2× in this paper · explained in 2 episodes
Ablation — 1× in this paper · explained in 1 episode
Abstention — 1× in this paper · explained in 1 episode
Calibration — 1× in this paper · explained in 9 episodes
Composition — 1× in this paper · explained in 2 episodes
Contextual Awareness — 1× in this paper · explained in 2 episodes
Decoupling — 1× in this paper · explained in 2 episodes
Division of Labor — 1× in this paper · explained in 2 episodes
Failure Modes — 1× in this paper · explained in 3 episodes
Faithfulness — 1× in this paper · explained in 2 episodes
Fidelity — 1× in this paper · explained in 2 episodes
Frozen model — 1× in this paper · explained in 3 episodes
Generalizability — 1× in this paper · explained in 6 episodes
Introspection — 1× in this paper · explained in 2 episodes
Latent Representation — 1× in this paper · explained in 2 episodes
Latent Space — 1× in this paper · explained in 7 episodes
Localization — 1× in this paper · explained in 2 episodes
Model Interpretability — 1× in this paper · explained in 2 episodes
Papers — 1× in this paper · explained in 3 episodes
Position Bias — 1× in this paper · explained in 2 episodes
Positional Bias — 1× in this paper · explained in 2 episodes
Prompt Engineering — 1× in this paper · explained in 6 episodes
Reinforcement Learning — 1× in this paper · explained in 4 episodes
Semantic Preservation — 1× in this paper · explained in 2 episodes
Transferability — 1× in this paper · explained in 5 episodes
Transformer Model — 1× in this paper · explained in 2 episodes
Transformer Models — 1× in this paper · explained in 3 episodes
Verification — 1× in this paper · explained in 2 episodes

Terminology

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