1 citations · 1 across the 6 of their papers we have counts for
6 papers
Jailbreaking Multimodal Large Language Models using Multi-Clip Video
Choongwon Kang, Seungjong Sun, Hyunmin Jun +1
As multimodal large language models (MLLMs) have advanced to process video inputs, concerns have emerged about their potential for malicious misuse. Prior jailbreak studies have sh…
LensVLM: Selective Context Expansion for Compressed Visual Representation of Text
Roy Xie, Dan Friedman, Donghan Yu +7
Vision Language Models (VLMs) offer the exciting possibility of processing text as rendered images, bypassing the need for tokenizing the text into long token sequences. Since VLM…
Fast KVzip: Efficient and Accurate LLM Inference with Gated KV Eviction
Jang-Hyun Kim, Dongyoon Han, Sangdoo Yun
Efficient key-value (KV) cache management is crucial for the practical deployment of large language models (LLMs), yet existing compression techniques often incur a trade-off betwe…
KVzip: Query-Agnostic KV Cache Compression with Context Reconstruction
Jang-Hyun Kim, Jinuk Kim, Sangwoo Kwon +3
Transformer-based large language models (LLMs) cache context as key-value (KV) pairs during inference. As context length grows, KV cache sizes expand, leading to substantial memory…
Large-Scale Targeted Cause Discovery via Learning from Simulated Data
Jang-Hyun Kim, Claudia Skok Gibbs, Sangdoo Yun +2
We propose a novel machine learning approach for inferring causal variables of a target variable from observations. Our focus is on directly inferring a set of causal factors witho…
Compressed Context Memory For Online Language Model Interaction
Jang-Hyun Kim, Junyoung Yeom, Sangdoo Yun +1
This paper presents a context key/value compression method for Transformer language models in online scenarios, where the context continually expands. As the context lengthens, the…