2 papers
cs.AI2026
REAM: Merging Improves Pruning of Experts in LLMs
Saurav Jha, Maryam Hashemzadeh, Ali Saheb Pasand +3
Mixture-of-Experts (MoE) large language models (LLMs) are among the top-performing architectures. The largest models, often with hundreds of billions of parameters, pose significan…
eess.AS2020
Adaptable Multi-Domain Language Model for Transformer ASR
Taewoo Lee, Min-Joong Lee, Tae Gyoon Kang +10
We propose an adapter based multi-domain Transformer based language model (LM) for Transformer ASR. The model consists of a big size common LM and small size adapters. The model ca…