2 citations · 2 across the 10 of their papers we have counts for
6 papers · 1 filter
MATCH: Modulating Attention via In-Context Retrieval for Long-Context Transformers
Linrui Ma, Chun Hei Lo, Xinyu Wang +12
The quadratic computational cost of traditional attention mechanisms poses a major bottleneck to the scalability and practical deployment of large language models (LLMs), particula…
BOSCH: Black-Box Binary Optimization for Short-Context Attention-Head Selection in LLMs
Abbas Ghaddar, Ivan Kobyzev, Boxing Chen +1
Post-training hybridization of large language models (LLMs) often replaces quadratic self-attention with sliding-window attention (SWA) to reduce KV cache usage and improve latency…
PoTPTQ: A Two-step Power-of-Two Post-training for LLMs
Xinyu Wang, Vahid Partovi Nia, Peng Lu +4
Large Language Models (LLMs) have demonstrated remarkable performance across various natural language processing (NLP) tasks. However, their deployment is challenging due to the su…
Resona: Improving Context Copying in Linear Recurrence Models with Retrieval
Xinyu Wang, Linrui Ma, Jerry Huang +5
Recent shifts in the space of large language model (LLM) research have shown an increasing focus on novel architectures to compete with prototypical Transformer-based models that h…
AdpQ: A Zero-shot Calibration Free Adaptive Post Training Quantization Method for LLMs
Alireza Ghaffari, Sharareh Younesian, Vahid Partovi Nia +2
The ever-growing computational complexity of Large Language Models (LLMs) necessitates efficient deployment strategies. The current state-of-the-art approaches for Post-training Qu…
Mitigating Outlier Activations in Low-Precision Fine-Tuning of Language Models
Alireza Ghaffari, Justin Yu, Mahsa Ghazvini Nejad +3
Low-precision fine-tuning of language models has gained prominence as a cost-effective and energy-efficient approach to deploying large-scale models in various applications. Howeve…