3 citations · 3 across the 19 of their papers we have counts for
19 papers
Faster Than Flash: Exploiting Attention Sparsity for Efficient Long-Context Decoding
Zhigeng Liu, Zhiyuan Ning, Ruixiao Li +5
The development of long-context Large Language Models (LLMs) is constrained by the memory bandwidth bottleneck and quadratic complexity of the attention mechanism during decoding.…
Prism: Spectral-Aware Block-Sparse Attention
Xinghao Wang, Pengyu Wang, Xiaoran Liu +4
Block-sparse attention is promising for accelerating long-context LLM pre-filling, yet identifying relevant blocks efficiently remains a bottleneck. Existing methods typically empl…
AgentLongBench: A Controllable Long Benchmark For Long-Contexts Agents via Environment Rollouts
Shicheng Fang, Yuxin Wang, Xiaoran Liu +6
The evolution of Large Language Models (LLMs) into autonomous agents necessitates the management of extensive, dynamic contexts. Current benchmarks, however, remain largely static,…
MrRoPE: Mixed-radix Rotary Position Embedding
Qingyuan Tian, Wenhong Zhu, Xiaoran Liu +2
Rotary Position Embedding (RoPE)-extension refers to modifying or generalizing the Rotary Position Embedding scheme to handle longer sequences than those encountered during pre-tra…
DiRL: An Efficient Post-Training Framework for Diffusion Language Models
Ying Zhu, Jiaxin Wan, Xiaoran Liu +7
Diffusion Language Models (dLLMs) have emerged as promising alternatives to Auto-Regressive (AR) models. While recent efforts have validated their pre-training potential and accele…
FourierSampler: Unlocking Non-Autoregressive Potential in Diffusion Language Models via Frequency-Guided Generation
Siyang He, Qiqi Wang, Xiaoran Liu +8
Despite the non-autoregressive potential of diffusion language models (dLLMs), existing decoding strategies demonstrate positional bias, failing to fully unlock the potential of ar…
Beyond Real: Imaginary Extension of Rotary Position Embeddings for Long-Context LLMs
Xiaoran Liu, Yuerong Song, Zhigeng Liu +6
Rotary Position Embeddings (RoPE) have become a standard for encoding sequence order in Large Language Models (LLMs) by applying rotations to query and key vectors in the complex p…
SIM-CoT: Supervised Implicit Chain-of-Thought
Xilin Wei, Xiaoran Liu, Yuhang Zang +5
Implicit Chain-of-Thought (CoT) methods offer a token-efficient alternative to explicit CoT reasoning in Large Language Models (LLMs), but a persistent performance gap has limited…
Sparse-dLLM: Accelerating Diffusion LLMs with Dynamic Cache Eviction
Yuerong Song, Xiaoran Liu, Ruixiao Li +5
Diffusion Large Language Models (dLLMs) enable breakthroughs in reasoning and parallel decoding but suffer from prohibitive quadratic computational complexity and memory overhead d…
Beyond Homogeneous Attention: Memory-Efficient LLMs via Fourier-Approximated KV Cache
Xiaoran Liu, Siyang He, Qiqi Wang +9
Large Language Models struggle with memory demands from the growing Key-Value (KV) cache as context lengths increase. Existing compression methods homogenize head dimensions or rel…
LongLLaDA: Unlocking Long Context Capabilities in Diffusion LLMs
Xiaoran Liu, Yuerong Song, Zhigeng Liu +4
Large Language Diffusion Models, or diffusion LLMs, have emerged as a significant focus in NLP research, with substantial effort directed toward understanding their scalability and…
Thus Spake Long-Context Large Language Model
Xiaoran Liu, Ruixiao Li, Mianqiu Huang +11
Long context is an important topic in Natural Language Processing (NLP), running through the development of NLP architectures, and offers immense opportunities for Large Language M…
VideoRoPE: What Makes for Good Video Rotary Position Embedding?
Xilin Wei, Xiaoran Liu, Yuhang Zang +9
While Rotary Position Embedding (RoPE) and its variants are widely adopted for their long-context capabilities, the extension of the 1D RoPE to video, with its complex spatio-tempo…
LongSafety: Enhance Safety for Long-Context LLMs
Mianqiu Huang, Xiaoran Liu, Shaojun Zhou +11
Recent advancements in model architectures and length extrapolation techniques have significantly extended the context length of large language models (LLMs), paving the way for th…
DetectiveQA: Evaluating Long-Context Reasoning on Detective Novels
Zhe Xu, Jiasheng Ye, Xiaoran Liu +8
Recently, significant efforts have been devoted to enhancing the long-context capabilities of Large Language Models (LLMs), particularly in long-context reasoning. To facilitate th…
ReAttention: Training-Free Infinite Context with Finite Attention Scope
Xiaoran Liu, Ruixiao Li, Qipeng Guo +7
The long-context capability of the Large Language Models (LLM) has made significant breakthroughs, but the maximum supported context length in length extrapolation remains a critic…
LongWanjuan: Towards Systematic Measurement for Long Text Quality
Kai Lv, Xiaoran Liu, Qipeng Guo +4
The quality of training data are crucial for enhancing the long-text capabilities of foundation models. Despite existing efforts to refine data quality through heuristic rules and…
CoLLiE: Collaborative Training of Large Language Models in an Efficient Way
Kai Lv, Shuo Zhang, Tianle Gu +11
Large language models (LLMs) are increasingly pivotal in a wide range of natural language processing tasks. Access to pre-trained models, courtesy of the open-source community, has…
Scaling Laws of RoPE-based Extrapolation
Xiaoran Liu, Hang Yan, Shuo Zhang +3
The extrapolation capability of Large Language Models (LLMs) based on Rotary Position Embedding is currently a topic of considerable interest. The mainstream approach to addressing…