8 citations · 9 across the 10 of their papers we have counts for
4 papers · 1 filter
Does a Global Perspective Help Prune Sparse MoEs Elegantly?
Zeliang Zhang, Nikhil Ghosh, Jiani Liu +2
Empirical scaling laws for language models have encouraged the development of ever-larger LLMs, despite their growing computational and memory costs. Sparse Mixture-of-Experts (MoE…
Why Instruction-Based Unlearning Fails in Diffusion Models?
Zeliang Zhang, Rui Sun, Jiani Liu +2
Instruction-based unlearning has proven effective for modifying the behavior of large language models at inference time, but whether this paradigm extends to other generative model…
OPENXRD: A Comprehensive Benchmark Framework for LLM/MLLM XRD Question Answering
Ali Vosoughi, Ayoub Shahnazari, Yufeng Xi +4
We introduce OPENXRD, a comprehensive benchmarking framework for evaluating large language models (LLMs) and multimodal LLMs (MLLMs) in crystallography question answering. The fram…
Diversifying the Expert Knowledge for Task-Agnostic Pruning in Sparse Mixture-of-Experts
Zeliang Zhang, Xiaodong Liu, Hao Cheng +2
By increasing model parameters but activating them sparsely when performing a task, the use of Mixture-of-Experts (MoE) architecture significantly improves the performance of Large…