12 citations · 22 across the 18 of their papers we have counts for
9 papers · 1 filter
Towards LifeSpan Cognitive Systems
Yu Wang, Chi Han, Tongtong Wu +9
Building a human-like system that continuously interacts with complex environments -- whether simulated digital worlds or human society -- presents several key challenges. Central…
Large Scale Knowledge Washing
Yu Wang, Ruihan Wu, Zexue He +2
Large language models show impressive abilities in memorizing world knowledge, which leads to concerns regarding memorization of private information, toxic or sensitive knowledge,…
Cognitive Bias in Decision-Making with LLMs
Jessica Echterhoff, Yao Liu, Abeer Alessa +2
Large language models (LLMs) offer significant potential as tools to support an expanding range of decision-making tasks. Given their training on human (created) data, LLMs have be…
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…
FINEST: Stabilizing Recommendations by Rank-Preserving Fine-Tuning
Sejoon Oh, Berk Ustun, Julian McAuley +1
Modern recommender systems may output considerably different recommendations due to small perturbations in the training data. Changes in the data from a single user will alter the…
MEMORYLLM: Towards Self-Updatable Large Language Models
Yu Wang, Yifan Gao, Xiusi Chen +9
Existing Large Language Models (LLMs) usually remain static after deployment, which might make it hard to inject new knowledge into the model. We aim to build models containing a c…
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…
DITTO: Diffusion Inference-Time T-Optimization for Music Generation
Zachary Novack, Julian McAuley, Taylor Berg-Kirkpatrick +1
We propose Diffusion Inference-Time T-Optimization (DITTO), a general-purpose frame-work for controlling pre-trained text-to-music diffusion models at inference-time via optimizing…