1 citations · 1 across the 8 of their papers we have counts for
8 papers
REAL-Q: E2E LLM Quantization via Dynamic Gradient Descent
Qian Zhang, Yaoming Li, Zhewen Tan +9
Post-training quantization (PTQ) is essential for deploying large language models (LLMs) under strict resource constraints. State-of-the-art PTQ methods quantize each layer with a…
Preserving Speech-to-Text LLM Capabilities in Speech-to-Speech Generation
Yuxuan Hu, Heng Lu, Ruchao Fan +8
Strong speech-to-text (S2T) LLMs already provide robust speech perception and text reasoning, but adding speech-to-speech (S2S) output is challenging: fine-tuning the backbone can…
Closing the Modality Reasoning Gap for Speech Large Language Models
Chaoren Wang, Heng Lu, Xueyao Zhang +4
Although Speech Large Language Models have achieved notable progress, a substantial modality reasoning gap remains: their reasoning performance on speech inputs is markedly weaker…
FlexiCodec: A Dynamic Neural Audio Codec for Low Frame Rates
Jiaqi Li, Yao Qian, Yuxuan Hu +7
Neural audio codecs are foundational to speech language models. It is expected to have a low frame rate and decoupled semantic and acoustic information. A lower frame rate codec ca…
Towards Efficient Speech-Text Jointly Decoding within One Speech Language Model
Haibin Wu, Yuxuan Hu, Ruchao Fan +8
Speech language models (Speech LMs) enable end-to-end speech-text modeling within a single model, offering a promising direction for spoken dialogue systems. The choice of speech-t…
SLM-S2ST: A multimodal language model for direct speech-to-speech translation
Yuxuan Hu, Haibin Wu, Ruchao Fan +4
Speech-aware language models (LMs) have demonstrated capabilities in understanding spoken language while generating text-based responses. However, enabling them to produce speech o…
FlashAudio: Rectified Flows for Fast and High-Fidelity Text-to-Audio Generation
Huadai Liu, Jialei Wang, Rongjie Huang +4
Recent advancements in latent diffusion models (LDMs) have markedly enhanced text-to-audio generation, yet their iterative sampling processes impose substantial computational deman…
The Impact of Quantization and Pruning on Deep Reinforcement Learning Models
Heng Lu, Mehdi Alemi, Reza Rawassizadeh
Deep reinforcement learning (DRL) has achieved remarkable success across various domains, such as video games, robotics, and, recently, large language models. However, the computat…