7 citations · 7 across the 13 of their papers we have counts for
10 papers · 1 filter
LazyFlash: Forward-Dense, Backward-Sparse Attention for Accelerated Long-Context Fine-Tuning
Keqi Deng, Ruchao Fan, Jinyu Li
As large language models (LLMs) process increasingly long sequences, attention becomes a major computational bottleneck. Sparse attention mostly targets inference, yet many methods…
Teaching LLMs to Hear Who Spoke What: Metadata-Supervised Pretraining for Encoder-Free Speech-LLMs
Mohan Shi, Ruchao Fan, Sunit Sivasankaran +2
Encoder-based speech large language models (Speech-LLMs) commonly employ pretrained speech encoders that prioritize linguistic content but may discard fine-grained acoustic cues es…
Do LLMs Need Architectural Changes for Simultaneous Speech Translation? A Prefix-to-Prefix Data Driven Approach
Junkun Chen, Jian Xue, Ming Tang +4
Simultaneous speech translation (SimulST) requires incremental translation under strict latency constraints, yet remains challenging for decoder-only LLM systems due to limited con…
Compress the Cache, Not the Speech Embedding: KV Compression for Efficient Speech LLMs
Ke-Han Lu, Keqi Deng, Ruchao Fan +2
Speech large language models (Speech LLMs) typically encode speech into sequences far longer than text, creating a major efficiency bottleneck during autoregressive decoding. A com…
Rethinking Speech-LLM Integration for ASR: Effective Joint Speech-Text Training by Interleaving
Ruchao Fan, Yiming Wang, Rui Zhao +10
Speech-LLM integration has shown promising results by leveraging extensive textual pretraining, yet its specific benefits for automatic speech recognition (ASR) remain unclear. We…
LLM can Read Spectrogram: Encoder-free Speech-Language Modeling
Ruchao Fan, Yiming Wang, Yuxuan Hu +6
Recent speech-aware large language models (Speech-LLMs) rely on pre-trained speech encoders to convert audio into semantic/acoustic rich representations consumable by LLM. In this…
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…
UNIQUE: Universal Top-k Sparse Attention for Training-free Inference and Sparsity-aware Training
Keqi Deng, Shaoshi Ling, Ruchao Fan +1
Long-context inference in large language models (LLMs) is bottlenecked by the linear growth of the self-attention key-value (KV) cache. Top-k sparse attention alleviates this by lo…
Speech LLMs are Contextual Reasoning Transcribers
Keqi Deng, Ruchao Fan, Bo Ren +2
Despite extensions to speech inputs, effectively leveraging the rich knowledge and contextual understanding of large language models (LLMs) in automatic speech recognition (ASR) re…
RLBR: Reinforcement Learning with Biasing Rewards for Contextual Speech Large Language Models
Bo Ren, Ruchao Fan, Yelong Shen +2
Speech large language models (LLMs) have driven significant progress in end-to-end speech understanding and recognition, yet they continue to struggle with accurately recognizing r…