Papers with personalized PEFT
Personalized Pieces: Efficient Personalized Large Language Models through Collaborative Efforts (2024.emnlp-main)
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| Challenge: | Experimental results show Per-Pcs outperforms non-personalized and PEFT retrieval baselines, offering performance comparable to OPPU with significantly lower resource use across six tasks. |
| Approach: | They propose a framework that allows users to safely share and assemble personalized large language models using their history data. |
| Outcome: | Experimental results show that Per-Pcs outperforms non-personalized and PEFT retrieval baselines with significantly lower resource use across six tasks. |