TICL improves style personalization by iteratively adding model-generated negative examples and explanations to an in-context prompt, beating fine-tuned baselines in LLM-judged comparisons without any parameter updates.
In Pro- ceedings of the 7th Workshop on Representation Learning for NLP, pages 249–268, Dublin, Ireland
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Tuning-Free Personalized Alignment via Trial-Error-Explain In-Context Learning
TICL improves style personalization by iteratively adding model-generated negative examples and explanations to an in-context prompt, beating fine-tuned baselines in LLM-judged comparisons without any parameter updates.