AI Research
Sampling Reveals Style: Unsupervised, Training-Free Discovery of Prompt-Conditional Stylistic Axes in LLM Activations
Uncovering hidden stylistic dimensions in LLMs without the need for supervised training data.
Large language models (LLMs) encode rich stylistic structure in their hidden activations, but discovering which stylistic dimensions are salient for a given prompt typically requires supervised contrastive data. We present a training-free, prompt-conditional alternative: we repea...
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ArXiv CS.CL
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ArXiv CS.CL
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Generated insight by OnAirToday AI
This research demonstrates that stylistic control can be extracted directly from internal model activations without needing expensive supervised fine-tuning. By identifying these latent dimensions, developers can steer LLM output styles more precisely and efficiently than traditional prompting methods.
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