3 accepted papers
Low-Rank Adaptation (LoRA) has become a standard paradigm for parameter-efficient fine-tuning (PEFT). However, its low-rank constraint can limit its capacity to express complex weight updates, leaving a performance gap compared with full fine-tuning. Existing extensions improve expressivity, but the
With the rapid development of large language models (LLMs), traditional full-parameter fine-tuning methods have become increasingly expensive in terms of computational resources and time costs. For this reason, parameter efficient fine-tuning (PEFT) methods have emerged. Among them, Low-Rank Adaptat…
Recently, we have witnessed a significant performance boosting for dialogue response selection task achieved by Cross-Encoder based models. However, such models directly feed the concatenation of context and response into the pre-trained model for interactive inference, ignoring the comprehensively…