Trial-Error-Explain In-Context Learning for Personalized Text Generation

This paper introduce Trial-Error-Explain In-Context Learning (TICL), a method for personalizing large language models (LLMs) to match individual user writing styles without requiring model fine-tuning. TICL expands the in-context learning prompt by adding model-generated negative examples and explanations that highlight discrepancies from the target style. Evaluations demonstrate that TICL significantly outperforms existing methods in generating text that stylistically aligns with authors, indicating its effectiveness for tuning-free personalized text generation. The research highlights the importance of the explanation component and notes that the method's success depends on the LLM's ability to handle long contexts.