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Fall 2024
This thesis addresses the task of few-shot style-conditioned text generation using large language models (LLMs). We propose a novel, model-agnostic approach for adapting LLMs to arbitrary styles using a few text samples from a certain author. Instead of using pre-defined features, our method...
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