@article{liu2025alignment,title={Alignment Whack-a-Mole: Finetuning Activates Verbatim Recall of Copyrighted Books in Large Language Models},author={Liu, Xinyue and Mireshghallah, Niloofar and Ginsburg, Jane C. and Chakrabarty, Tuhin},year={2026},}
@inproceedings{zhang2025noveltybench,title={NoveltyBench: Evaluating Creativity and Diversity in Language Models},author={Zhang, Yiming and Diddee, Harshita and Holm, Susan and Liu, Hanchen and Liu, Xinyue and Samuel, Vinay and Wang, Barry and Ippolito, Daphne},booktitle={Second Conference on Language Modeling},url={https://openreview.net/forum?id=XZm1ekzERf},year={2025},}
One-size-fits-all large language models (LLMs) are increasingly being used to help people with their writing. However, the style these models are trained to write in may not suit all users or use cases. LLMs would be more useful as writing assistants if their idiolect could be customized to match each user. In this paper, we explore whether parameter-efficient finetuning (PEFT) with Low-Rank Adaptation can effectively guide the style of LLM generations. We use this method to customize LLaMA-2 to ten different authors and show that the generated text has lexical, syntactic, and surface alignment with the target author but struggles with content memorization. Our findings highlight the potential of PEFT to support efficient, user-level customization of LLMs.
@inproceedings{liu-etal-2024-customizing-large,title={Customizing Large Language Model Generation Style using Parameter-Efficient Finetuning},author={Liu, Xinyue and Diddee, Harshita and Ippolito, Daphne},editor={Mahamood, Saad and Minh, Nguyen Le and Ippolito, Daphne},booktitle={Proceedings of the 17th International Natural Language Generation Conference},year={2024},address={Tokyo, Japan},publisher={Association for Computational Linguistics},url={https://aclanthology.org/2024.inlg-main.34/},pages={412--426},}
In Second Conference on Language Modeling, 2025
In Proceedings of the 17th International Natural Language Generation Conference, 2024