4 papers
Better and Worse with Scale: How Contextual Entrainment Diverges with Model Size
Dikshant Kukreja, Kshitij Sah, Gautam Gupta +5
Larger language models become simultaneously better and worse at handling contextual information -- better at ignoring false claims, worse at ignoring irrelevant tokens. We formali…
IC-Cache: Efficient Large Language Model Serving via In-context Caching
Yifan Yu, Yu Gan, Nikhil Sarda +7
Large language models (LLMs) have excelled in various applications, yet serving them at scale is challenging due to their substantial resource demands and high latency. Our real-wo…
Toward Cost-Efficient Serving of Mixture-of-Experts with Asynchrony
Shaoyu Wang, Guangrong He, Geon-Woo Kim +2
Mixture-of-Experts (MoE) architectures offer the promise of larger model capacity without the prohibitive costs of fully dense designs. However, in real-world inference serving, lo…
Scalable Multi-Domain Adaptation of Language Models using Modular Experts
Peter Schafhalter, Shun Liao, Yanqi Zhou +3
Domain-specific adaptation is critical to maximizing the performance of pre-trained language models (PLMs) on one or multiple targeted tasks, especially under resource-constrained…