4 citations · 17 across the 34 of their papers we have counts for
6 papers · 1 filter
D2: High-Capability Full-Duplex Spoken Language Models from Half-Duplex Backbones
Cheng-Kuang Chang, Alexander H. Liu, Brian Kingsbury +5
Full-duplex spoken language models (FD-SLMs) support simultaneous listening and speaking, but often lag behind half-duplex spoken language models (HD-SLMs) in general capabilities.…
M+: Extending MemoryLLM with Scalable Long-Term Memory
Yu Wang, Dmitry Krotov, Yuanzhe Hu +6
Equipping large language models (LLMs) with latent-space memory has attracted increasing attention as they can extend the context window of existing language models. However, retai…
Self-MoE: Towards Compositional Large Language Models with Self-Specialized Experts
Junmo Kang, Leonid Karlinsky, Hongyin Luo +7
We present Self-MoE, an approach that transforms a monolithic LLM into a compositional, modular system of self-specialized experts, named MiXSE (MiXture of Self-specialized Experts…
Large Scale Generative AI Text Applied to Sports and Music
Aaron Baughman, Stephen Hammer, Rahul Agarwal +5
We address the problem of scaling up the production of media content, including commentary and personalized news stories, for large-scale sports and music events worldwide. Our app…
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…
Self-Specialization: Uncovering Latent Expertise within Large Language Models
Junmo Kang, Hongyin Luo, Yada Zhu +6
Recent works have demonstrated the effectiveness of self-alignment in which a large language model is aligned to follow general instructions using instructional data generated from…