6 citations · 13 across the 12 of their papers we have counts for
12 papers
On the Efficiency-Safety Dilemma in Large Reasoning Models
Yifei Yang, Zouying Cao, Xingrui Wang +4
Large reasoning models (LRMs) incur high inference costs, often mitigated by efficiency techniques like quantization and pruning. However, the impact of these techniques on model a…
TACT: Taxonomy-Aligned Post-Training for Pedagogically Adaptive English Tutoring
Dongjie Yang, Siyan Lin, Leixian Shen +3
Large language models (LLMs) are increasingly used to provide conversational practice for English-as-a-second-language (ESL) learners. Effective ESL tutoring, however, requires mor…
Wide-Horizon Thinking and Simulation-Based Evaluation for Real-World LLM Planning with Multifaceted Constraints
Dongjie Yang, Chengqiang Lu, Qimeng Wang +4
Unlike reasoning, which often entails a deep sequence of deductive steps, complex real-world planning is characterized by the need to synthesize a broad spectrum of parallel and po…
BriLLM: Brain-inspired Large Language Model
Hai Zhao, Hongqiu Wu, Dongjie Yang +2
We introduce BriLLM, a brain-inspired large language model that fundamentally redefines the foundations of machine learning through its implementation of Signal Fully-connected flo…
How Deep is Love in LLMs' Hearts? Exploring Semantic Size in Human-like Cognition
Yao Yao, Yifei Yang, Xinbei Ma +4
How human cognitive abilities are formed has long captivated researchers. However, a significant challenge lies in developing meaningful methods to measure these complex processes.…
KVSharer: Efficient Inference via Layer-Wise Dissimilar KV Cache Sharing
Yifei Yang, Zouying Cao, Qiguang Chen +4
The development of large language models (LLMs) has significantly expanded model sizes, resulting in substantial GPU memory requirements during inference. The key and value storage…
Are LLMs Aware that Some Questions are not Open-ended?
Dongjie Yang, Hai Zhao
Large Language Models (LLMs) have shown the impressive capability of answering questions in a wide range of scenarios. However, when LLMs face different types of questions, it is w…
PyramidInfer: Pyramid KV Cache Compression for High-throughput LLM Inference
Dongjie Yang, XiaoDong Han, Yan Gao +3
Large Language Models (LLMs) have shown remarkable comprehension abilities but face challenges in GPU memory usage during inference, hindering their scalability for real-time appli…
Vript: A Video Is Worth Thousands of Words
Dongjie Yang, Suyuan Huang, Chengqiang Lu +5
Advancements in multimodal learning, particularly in video understanding and generation, require high-quality video-text datasets for improved model performance. Vript addresses th…
BatGPT: A Bidirectional Autoregessive Talker from Generative Pre-trained Transformer
Zuchao Li, Shitou Zhang, Hai Zhao +2
BatGPT is a large-scale language model designed and trained jointly by Wuhan University and Shanghai Jiao Tong University. It is capable of generating highly natural and fluent tex…
RefGPT: Dialogue Generation of GPT, by GPT, and for GPT
Dongjie Yang, Ruifeng Yuan, Yuantao Fan +4
Large Language Models (LLMs) have attained the impressive capability to resolve a wide range of NLP tasks by fine-tuning high-quality instruction data. However, collecting human-wr…
Learning Better Masking for Better Language Model Pre-training
Dongjie Yang, Zhuosheng Zhang, Hai Zhao
Masked Language Modeling (MLM) has been widely used as the denoising objective in pre-training language models (PrLMs). Existing PrLMs commonly adopt a Random-Token Masking strateg…