13 citations · 19 across the 14 of their papers we have counts for
4 papers · 1 filter
Accelerating Prefilling via Decoding-time Contribution Sparsity
Zhiyuan He, Yike Zhang, Chengruidong Zhang +3
Large Language Models (LLMs) incur quadratic attention complexity with input length, creating a major time bottleneck in the prefilling stage. Existing acceleration methods largely…
Do Not Let Low-Probability Tokens Over-Dominate in RL for LLMs
Zhihe Yang, Xufang Luo, Zilong Wang +4
Reinforcement learning (RL) has become a cornerstone for enhancing the reasoning capabilities of large language models (LLMs), with recent innovations such as Group Relative Policy…
Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely
Siyun Zhao, Yuqing Yang, Zilong Wang +3
Large language models (LLMs) augmented with external data have demonstrated remarkable capabilities in completing real-world tasks. Techniques for integrating external data into LL…
Position Engineering: Boosting Large Language Models through Positional Information Manipulation
Zhiyuan He, Huiqiang Jiang, Zilong Wang +3
The performance of large language models (LLMs) is significantly influenced by the quality of the prompts provided. In response, researchers have developed enormous prompt engineer…