Task-specific LoRA adapters offer a modular way to specialize large language and vision-language models. However, existing adapter composition methods are mostly static and cannot adapt to individual test inputs. To address these issues, we propose \textbf{LiST}, a label-free test-time LoRA fusion framework that conver...
Yi-Hua Shao, Jia Li, Si-Yu Chen et al.· 2 citations
This paper introduces Gradient Enhancement Task Aware Post-training Quantization, i.e., GTAQ, to address the generalization issue of Large Language Models, and extensively evaluates the LLaMA family of language models on WikiText, C4, and MMLU.
Yi-Hua Shao, Yang-Yang Gu, Min-Xi Yan et al.· Proceedings of the Thirty-Fi...· 1 citation
Cross-Domain TTS is proposed, a novel framework that enables task-tailored scaling in broader domains and achieves an improvement of up to 17% in pass@1 accuracy while reducing inference latency and saving up to 30% in token consumption.
Minxi Yan, Yihua Shao, Yanling Pan et al.· 0 citations
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