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cs.CL2025
Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models
Zhijun Tu, Jian Li, Yuanyuan Xi +5
1-bit LLM quantization offers significant advantages in reducing storage and computational costs. However, existing methods typically train 1-bit LLMs from scratch, failing to full…
cs.CL2024★ 1 cited
Forest-of-Thought: Scaling Test-Time Compute for Enhancing LLM Reasoning
Zhenni Bi, Kai Han, Chuanjian Liu +2
Large Language Models (LLMs) have demonstrated remarkable abilities across various language tasks, but solving complex reasoning problems remains a significant challenge. While exi…
cs.CL2024
EMS-SD: Efficient Multi-sample Speculative Decoding for Accelerating Large Language Models
Yunsheng Ni, Chuanjian Liu, Yehui Tang +2
Speculative decoding emerges as a pivotal technique for enhancing the inference speed of Large Language Models (LLMs). Despite recent research aiming to improve prediction efficien…