1 citations · 1 across the 4 of their papers we have counts for
6 papers
Batch-of-Thought: Cross-Instance Learning for Enhanced LLM Reasoning
Xuan Yang, Furong Jia, Roy Xie +4
Current Large Language Model reasoning systems process queries independently, discarding valuable cross-instance signals such as shared reasoning patterns and consistency constrain…
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…
Dynamic Frequency-Adaptive Knowledge Distillation for Speech Enhancement
Xihao Yuan, Siqi Liu, Hanting Chen +3
Deep learning-based speech enhancement (SE) models have recently outperformed traditional techniques, yet their deployment on resource-constrained devices remains challenging due t…
MooER: LLM-based Speech Recognition and Translation Models from Moore Threads
Junhao Xu, Zhenlin Liang, Yi Liu +5
In this paper, we present MooER, a LLM-based large-scale automatic speech recognition (ASR) / automatic speech translation (AST) model of Moore Threads. A 5000h pseudo labeled data…
USM-Lite: Quantization and Sparsity Aware Fine-tuning for Speech Recognition with Universal Speech Models
Shaojin Ding, David Qiu, David Rim +10
End-to-end automatic speech recognition (ASR) models have seen revolutionary quality gains with the recent development of large-scale universal speech models (USM). However, deploy…
Aurora:Activating Chinese chat capability for Mixtral-8x7B sparse Mixture-of-Experts through Instruction-Tuning
Rongsheng Wang, Haoming Chen, Ruizhe Zhou +8
Existing research has demonstrated that refining large language models (LLMs) through the utilization of machine-generated instruction-following data empowers these models to exhib…