Parts-of-Speech as Emergent Categories in SAE Latent Space

arXiv:2609.29362 · cs.CL · Submitted 2026-09-24 · Read on arXiv

cs.CL

Submitted: 2026-09-24

Updated: 2026-09-24

Code: https://github.com/colinglab/pos-sae-latents

Terminology

• Latent Space — 20× in this paper · explained in 7 episodes
• Accuracy — 9× in this paper · explained in 1 episode
• Interpretability — 9× in this paper · explained in 15 episodes
• Adam — 6× in this paper · explained in 2 episodes
• Abstraction — 5× in this paper · explained in 1 episode
• Transformer — 4× in this paper · explained in 2 episodes
• F1 Score — 3× in this paper · explained in 3 episodes
• Alternation — 2× in this paper · explained in 1 episode
• Core — 2× in this paper · explained in 2 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Latent Representation — 2× in this paper · explained in 2 episodes
• Latent Representations — 2× in this paper · explained in 3 episodes
• Sparse Autoencoders (SAEs) — 2× in this paper · explained in 3 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Feature Selection — 1× in this paper · explained in 4 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Linear Probing — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Sparsity — 1× in this paper · explained in 4 episodes

Sources

• Sparse Autoencoders Find Highly Interpretable Features in Language Models
• The Llama 3 Herd of Models
• Llama Scope: Extracting Millions of Features from Llama-3.1-8B with Sparse Autoencoders
• Open Problems in Mechanistic Interpretability

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