4 papers
MATCH: Modulating Attention via In-Context Retrieval for Long-Context Transformers
Linrui Ma, Chun Hei Lo, Xinyu Wang +12
The quadratic computational cost of traditional attention mechanisms poses a major bottleneck to the scalability and practical deployment of large language models (LLMs), particula…
Near-Policy: Accelerating On-Policy Distillation via Asynchronous Generation and Selective Packing
Miao Rang, Zhenni Bi, Hang Zhou +6
Standard knowledge distillation for autoregressive models often suffers from distribution mismatch. While on-policy methods mitigate this by leveraging student-generated outputs, t…
Physics-Guided Multimodal Transformers are the Necessary Foundation for the Next Generation of Meteorological Science
Jing Han, Hanting Chen, Kai Han +4
This position paper argues that the next generation of artificial intelligence in meteorological and climate sciences must transition from fragmented hybrid heuristics toward a uni…
Transferable text data distillation by trajectory matching
Rong Yao, Hailin Hu, Yifei Fu +5
In the realm of large language model (LLM), as the size of large models increases, it also brings higher training costs. There is a urgent need to minimize the data size in LLM tra…