13 citations · 90 across the 40 of their papers we have counts for
13 papers · 1 filter
MTraining: Distributed Dynamic Sparse Attention for Efficient Ultra-Long Context Training
Wenxuan Li, Chengruidong Zhang, Huiqiang Jiang +3
The adoption of long context windows has become a standard feature in Large Language Models (LLMs), as extended contexts significantly enhance their capacity for complex reasoning…
Chain-of-Model Learning for Language Model
Kaitao Song, Xiaohua Wang, Xu Tan +14
In this paper, we propose a novel learning paradigm, termed Chain-of-Model (CoM), which incorporates the causal relationship into the hidden states of each layer as a chain style,…
On Memory Construction and Retrieval for Personalized Conversational Agents
Zhuoshi Pan, Qianhui Wu, Huiqiang Jiang +8
To deliver coherent and personalized experiences in long-term conversations, existing approaches typically perform retrieval augmented response generation by constructing memory ba…
SCBench: A KV Cache-Centric Analysis of Long-Context Methods
Yucheng Li, Huiqiang Jiang, Qianhui Wu +8
Long-context LLMs have enabled numerous downstream applications but also introduced significant challenges related to computational and memory efficiency. To address these challeng…
MInference 1.0: Accelerating Pre-filling for Long-Context LLMs via Dynamic Sparse Attention
Huiqiang Jiang, Yucheng Li, Chengruidong Zhang +9
The computational challenges of Large Language Model (LLM) inference remain a significant barrier to their widespread deployment, especially as prompt lengths continue to increase.…
Expressive and Generalizable Low-rank Adaptation for Large Models via Slow Cascaded Learning
Siwei Li, Yifan Yang, Yifei Shen +4
Efficient fine-tuning plays a fundamental role in modern large models, with low-rank adaptation emerging as a particularly promising approach. However, the existing variants of LoR…
Mitigate Position Bias in Large Language Models via Scaling a Single Dimension
Yijiong Yu, Huiqiang Jiang, Xufang Luo +6
Large Language Models (LLMs) are increasingly applied in various real-world scenarios due to their excellent generalization capabilities and robust generative abilities. However, t…
LLM-RadJudge: Achieving Radiologist-Level Evaluation for X-Ray Report Generation
Zilong Wang, Xufang Luo, Xinyang Jiang +2
Evaluating generated radiology reports is crucial for the development of radiology AI, but existing metrics fail to reflect the task's clinical requirements. This study proposes a…
LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression
Zhuoshi Pan, Qianhui Wu, Huiqiang Jiang +10
This paper focuses on task-agnostic prompt compression for better generalizability and efficiency. Considering the redundancy in natural language, existing approaches compress prom…
LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression
Huiqiang Jiang, Qianhui Wu, Xufang Luo +4
In long context scenarios, large language models (LLMs) face three main challenges: higher computational cost, performance reduction, and position bias. Research indicates that LLM…
LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models
Huiqiang Jiang, Qianhui Wu, Chin-Yew Lin +2
Large language models (LLMs) have been applied in various applications due to their astonishing capabilities. With advancements in technologies such as chain-of-thought (CoT) promp…
End-to-End Word-Level Pronunciation Assessment with MASK Pre-training
Yukang Liang, Kaitao Song, Shaoguang Mao +6
Pronunciation assessment is a major challenge in the computer-aided pronunciation training system, especially at the word (phoneme)-level. To obtain word (phoneme)-level scores, cu…
Accurate and Structured Pruning for Efficient Automatic Speech Recognition
Huiqiang Jiang, Li Lyna Zhang, Yuang Li +7
Automatic Speech Recognition (ASR) has seen remarkable advancements with deep neural networks, such as Transformer and Conformer. However, these models typically have large model s…