58 citations · 188 across the 90 of their papers we have counts for
19 papers · 1 filter
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…
ChatRex: Taming Multimodal LLM for Joint Perception and Understanding
Qing Jiang, Gen Luo, Yuqin Yang +5
Perception and understanding are two pillars of computer vision. While multimodal large language models (MLLM) have demonstrated remarkable visual understanding capabilities, they…
LLM2CLIP: Powerful Language Model Unlocks Richer Cross-Modality Representation
Weiquan Huang, Aoqi Wu, Yifan Yang +10
CLIP is a seminal multimodal model that maps images and text into a shared representation space through contrastive learning on billions of image-caption pairs. Inspired by the rap…
SPFresh: Incremental In-Place Update for Billion-Scale Vector Search
Yuming Xu, Hengyu Liang, Jin Li +9
Approximate Nearest Neighbor Search (ANNS) is now widely used in various applications, ranging from information retrieval, question answering, and recommendation, to search for sim…
ReMe: Scaffolding Personalized Cognitive Training via Controllable LLM-Mediated Conversations
Zilong Wang, Nan Chen, Luna K. Qiu +6
Global aging calls for scalable and engaging cognitive interventions. Computerized cognitive training (CCT) is a promising non-pharmacological approach, yet many unsupervised progr…
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…
RetrievalAttention: Accelerating Long-Context LLM Inference via Vector Retrieval
Di Liu, Meng Chen, Baotong Lu +11
Transformer-based Large Language Models (LLMs) have become increasingly important. However, due to the quadratic time complexity of attention computation, scaling LLMs to longer co…
VisEval: A Benchmark for Data Visualization in the Era of Large Language Models
Nan Chen, Yuge Zhang, Jiahang Xu +2
Translating natural language to visualization (NL2VIS) has shown great promise for visual data analysis, but it remains a challenging task that requires multiple low-level implemen…
A Queueing Theoretic Perspective on Low-Latency LLM Inference with Variable Token Length
Yuqing Yang, Yuedong Xu, Lei Jiao
Large language models (LLMs) propel the prosperity of interactive AI applications showcased by ChatGPT that demand timely response of inference services. However, LLM inference is…
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…
Parrot: Efficient Serving of LLM-based Applications with Semantic Variable
Chaofan Lin, Zhenhua Han, Chengruidong Zhang +4
The rise of large language models (LLMs) has enabled LLM-based applications (a.k.a. AI agents or co-pilots), a new software paradigm that combines the strength of LLM and conventio…
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…
Designing Network Algorithms via Large Language Models
Zhiyuan He, Aashish Gottipati, Lili Qiu +4
We introduce NADA, the first framework to autonomously design network algorithms by leveraging the generative capabilities of large language models (LLMs). Starting with an existin…
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…
Automated Contrastive Learning Strategy Search for Time Series
Baoyu Jing, Yansen Wang, Guoxin Sui +5
In recent years, Contrastive Learning (CL) has become a predominant representation learning paradigm for time series. Most existing methods manually build specific CL Strategies (C…
Benchmarking Data Science Agents
Yuge Zhang, Qiyang Jiang, Xingyu Han +3
In the era of data-driven decision-making, the complexity of data analysis necessitates advanced expertise and tools of data science, presenting significant challenges even for spe…
On the Out-Of-Distribution Generalization of Multimodal Large Language Models
Xingxuan Zhang, Jiansheng Li, Wenjing Chu +6
We investigate the generalization boundaries of current Multimodal Large Language Models (MLLMs) via comprehensive evaluation under out-of-distribution scenarios and domain-specifi…