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Sampling Reveals Style: Unsupervised, Training-Free Discovery of Prompt-Conditional Stylistic Axes in LLM Activations
Not provided in the abstract
PCALLMstylistic analysisunsupervised learning
2609.19150
Builder Relevance
2h ago70%
Abstract
This paper presents a training-free method for discovering stylistic dimensions in LLM activations using PCA on sampled completions.
Reality Card
Core Claim
The study demonstrates that PCA can effectively reveal stylistic axes in LLMs without the need for supervised data, achieving notable precision and recall in identifying human-salient stylistic dimensions.
Method / Result
Achieved 72.8% precision and 43.6% macro-recall in matching discovered axes to human-elicited stylistic annotations.
Limitations
Discoverability is strongly model-dependent, with significant variability in performance across different LLMs.
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