9 citations · 14 across the 9 of their papers we have counts for
9 papers
CAMELoT: Towards Large Language Models with Training-Free Consolidated Associative Memory
Zexue He, Leonid Karlinsky, Donghyun Kim +3
Large Language Models (LLMs) struggle to handle long input sequences due to high memory and runtime costs. Memory-augmented models have emerged as a promising solution to this prob…
LVCHAT: Facilitating Long Video Comprehension
Yu Wang, Zeyuan Zhang, Julian McAuley +1
Enabling large language models (LLMs) to read videos is vital for multimodal LLMs. Existing works show promise on short videos whereas long video (longer than e.g.~1 minute) compre…
InfoRank: Unbiased Learning-to-Rank via Conditional Mutual Information Minimization
Jiarui Jin, Zexue He, Mengyue Yang +4
Ranking items regarding individual user interests is a core technique of multiple downstream tasks such as recommender systems. Learning such a personalized ranker typically relies…
MedEval: A Multi-Level, Multi-Task, and Multi-Domain Medical Benchmark for Language Model Evaluation
Zexue He, Yu Wang, An Yan +5
Curated datasets for healthcare are often limited due to the need of human annotations from experts. In this paper, we present MedEval, a multi-level, multi-task, and multi-domain…
Farzi Data: Autoregressive Data Distillation
Noveen Sachdeva, Zexue He, Wang-Cheng Kang +3
We study data distillation for auto-regressive machine learning tasks, where the input and output have a strict left-to-right causal structure. More specifically, we propose Farzi,…
Robust and Interpretable Medical Image Classifiers via Concept Bottleneck Models
An Yan, Yu Wang, Yiwu Zhong +8
Medical image classification is a critical problem for healthcare, with the potential to alleviate the workload of doctors and facilitate diagnoses of patients. However, two challe…