Tuning-Free Personalized Alignment via Trial-Error-Explain In-Context Learning (2025.findings-naacl)
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Hyundong Justin Cho, Karishma Sharma, Nicolaas Paul Jedema, Leonardo F. R. Ribeiro, Jonathan May, Alessandro Moschitti
| Challenge: | Language models are biased towards generic outputs as they are trained to align to an aggregate preference to be generally useful. |
| Approach: | They propose a tuning-free method that personalizes language models for text generation tasks with fewer than 10 examples per user. |
| Outcome: | The proposed method achieves favorable win rates on pairwise comparisons with the previous state-of-the-art and outperforms competitive tuning-free baselines for personalized alignment tasks of writing emails, essays and news articles. |
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