3 citations · 12 across the 17 of their papers we have counts for
8 papers · 1 filter
SpeechPrune: Context-aware Token Pruning for Speech Information Retrieval
Yueqian Lin, Yuzhe Fu, Jingyang Zhang +5
We introduce Speech Information Retrieval (SIR), a new long-context task for Speech Large Language Models (Speech LLMs), and present SPIRAL, a 1,012-sample benchmark testing models…
Sufficient Context: A New Lens on Retrieval Augmented Generation Systems
Hailey Joren, Jianyi Zhang, Chun-Sung Ferng +3
Augmenting LLMs with context leads to improved performance across many applications. Despite much research on Retrieval Augmented Generation (RAG) systems, an open question is whet…
SLED: Self Logits Evolution Decoding for Improving Factuality in Large Language Models
Jianyi Zhang, Da-Cheng Juan, Cyrus Rashtchian +3
Large language models (LLMs) have demonstrated remarkable capabilities, but their outputs can sometimes be unreliable or factually incorrect. To address this, we introduce Self Log…
CoreInfer: Accelerating Large Language Model Inference with Semantics-Inspired Adaptive Sparse Activation
Qinsi Wang, Saeed Vahidian, Hancheng Ye +3
Large language models (LLMs) with billions of parameters have sparked a new wave of exciting AI applications. However, their high computational costs and memory demands during infe…
Federated Large Language Models: Current Progress and Future Directions
Yuhang Yao, Jianyi Zhang, Junda Wu +11
Large Language Models have achieved impressive performance across diverse applications, yet their training typically depends on centralized data collection, raising serious privacy…
MLLM-LLaVA-FL: Multimodal Large Language Model Assisted Federated Learning
Jianyi Zhang, Hao Frank Yang, Ang Li +5
Previous studies on federated learning (FL) often encounter performance degradation due to data heterogeneity among different clients. In light of the recent advances in multimodal…
ARTIST: Improving the Generation of Text-rich Images with Disentangled Diffusion Models and Large Language Models
Jianyi Zhang, Yufan Zhou, Jiuxiang Gu +5
Diffusion models have demonstrated exceptional capabilities in generating a broad spectrum of visual content, yet their proficiency in rendering text is still limited: they often g…
Min-K%++: Improved Baseline for Detecting Pre-Training Data from Large Language Models
Jingyang Zhang, Jingwei Sun, Eric Yeats +5
The problem of pre-training data detection for large language models (LLMs) has received growing attention due to its implications in critical issues like copyright violation and t…