5 citations · 6 across the 14 of their papers we have counts for
14 papers
Ltri-LLM: Streaming Long Context Inference for LLMs with Training-Free Dynamic Triangular Attention Pattern
Hongyin Tang, Di Xiu, Lanrui Wang +3
The quadratic computational complexity of the attention mechanism in current Large Language Models (LLMs) renders inference with long contexts prohibitively expensive. To address t…
Predictor-Corrector Enhanced Transformers with Exponential Moving Average Coefficient Learning
Bei Li, Tong Zheng, Rui Wang +8
Residual networks, as discrete approximations of Ordinary Differential Equations (ODEs), have inspired significant advancements in neural network design, including multistep method…
Multi-Programming Language Sandbox for LLMs
Shihan Dou, Jiazheng Zhang, Jianxiang Zang +25
We introduce MPLSandbox, an out-of-the-box multi-programming language sandbox designed to provide unified and comprehensive feedback from compiler and analysis tools for Large Lang…
Mitigating Tail Narrowing in LLM Self-Improvement via Socratic-Guided Sampling
Yiwen Ding, Zhiheng Xi, Wei He +7
Self-improvement methods enable large language models (LLMs) to generate solutions themselves and iteratively train on filtered, high-quality rationales. This process proves effect…
FIRP: Faster LLM inference via future intermediate representation prediction
Pengfei Wu, Jiahao Liu, Zhuocheng Gong +5
Recent advancements in Large Language Models (LLMs) have shown remarkable performance across a wide range of tasks. Despite this, the auto-regressive nature of LLM decoding, which…
Hallu-PI: Evaluating Hallucination in Multi-modal Large Language Models within Perturbed Inputs
Peng Ding, Jingyu Wu, Jun Kuang +6
Multi-modal Large Language Models (MLLMs) have demonstrated remarkable performance on various visual-language understanding and generation tasks. However, MLLMs occasionally genera…