5 papers
FAQ: Mitigating Quantization Error via Regenerating Calibration Data with Family-Aware Quantization
Haiyang Xiao, Weiqing Li, Jinyue Guo +3
Although post-training quantization (PTQ) provides an efficient numerical compression scheme for deploying large language models (LLMs) on resource-constrained devices, the represe…
MM-R1: Unleashing the Power of Unified Multimodal Large Language Models for Personalized Image Generation
Qian Liang, Yujia Wu, Kuncheng Li +4
Multimodal Large Language Models (MLLMs) with unified architectures excel across a wide range of vision-language tasks, yet aligning them with personalized image generation remains…
HASH-RAG: Bridging Deep Hashing with Retriever for Efficient, Fine Retrieval and Augmented Generation
Jinyu Guo, Xunlei Chen, Qiyang Xia +5
Retrieval-Augmented Generation (RAG) encounters efficiency challenges when scaling to massive knowledge bases while preserving contextual relevance. We propose Hash-RAG, a framewor…
Accelerating Adaptive Retrieval Augmented Generation via Instruction-Driven Representation Reduction of Retrieval Overlaps
Jie Ou, Jinyu Guo, Shuaihong Jiang +4
Retrieval-augmented generation (RAG) has emerged as a pivotal method for expanding the knowledge of large language models. To handle complex queries more effectively, researchers d…
Noise-Robustness Through Noise: A Framework combining Asymmetric LoRA with Poisoning MoE
Zhaokun Wang, Jinyu Guo, Jingwen Pu +5
Current parameter-efficient fine-tuning methods for adapting pre-trained language models to downstream tasks are susceptible to interference from noisy data. Conventional noise-han…