3 citations · 4 across the 10 of their papers we have counts for
10 papers
NeuPAT: Neuron-aware Plasticity Allocation Tuning for Language-Preserving MLLMs
Jiayue Jin, Jingwei Zhang, Chen Wang +2
Multimodal expansion of large language models (LLMs) enables new perceptual capabilities but often compromises the language intelligence acquired during pretraining. In this work,…
Look Again, Think Slowly: Enhancing Visual Reflection in Vision-Language Models
Pu Jian, Junhong Wu, Wei Sun +3
Recent advances in text-only "slow-thinking" reasoning have prompted efforts to transfer this capability to vision-language models (VLMs), for training visual reasoning models (\te…
OpenS2S: Advancing Fully Open-Source End-to-End Empathetic Large Speech Language Model
Chen Wang, Tianyu Peng, Wen Yang +8
Empathetic interaction is a cornerstone of human-machine communication, due to the need for understanding speech enriched with paralinguistic cues and generating emotional and expr…
Implicit Cross-Lingual Rewarding for Efficient Multilingual Preference Alignment
Wen Yang, Junhong Wu, Chen Wang +2
Direct Preference Optimization (DPO) has become a prominent method for aligning Large Language Models (LLMs) with human preferences. While DPO has enabled significant progress in a…
Language Imbalance Driven Rewarding for Multilingual Self-improving
Wen Yang, Junhong Wu, Chen Wang +2
Large Language Models (LLMs) have achieved state-of-the-art performance across numerous tasks. However, these advancements have predominantly benefited "first-class" languages such…
BLSP-Emo: Towards Empathetic Large Speech-Language Models
Chen Wang, Minpeng Liao, Zhongqiang Huang +3
The recent release of GPT-4o showcased the potential of end-to-end multimodal models, not just in terms of low latency but also in their ability to understand and generate expressi…
Diver: Large Language Model Decoding with Span-Level Mutual Information Verification
Jinliang Lu, Chen Wang, Jiajun Zhang
Large language models (LLMs) have shown impressive capabilities in adapting to various tasks when provided with task-specific instructions. However, LLMs using standard decoding st…
BLSP-KD: Bootstrapping Language-Speech Pre-training via Knowledge Distillation
Chen Wang, Minpeng Liao, Zhongqiang Huang +1
Recent end-to-end approaches have shown promise in extending large language models (LLMs) to speech inputs, but face limitations in directly assessing and optimizing alignment qual…
BLSP: Bootstrapping Language-Speech Pre-training via Behavior Alignment of Continuation Writing
Chen Wang, Minpeng Liao, Zhongqiang Huang +5
The emergence of large language models (LLMs) has sparked significant interest in extending their remarkable language capabilities to speech. However, modality alignment between sp…
Discrete Cross-Modal Alignment Enables Zero-Shot Speech Translation
Chen Wang, Yuchen Liu, Boxing Chen +4
End-to-end Speech Translation (ST) aims at translating the source language speech into target language text without generating the intermediate transcriptions. However, the trainin…