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Zican Dong

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NeurIPS Conference 2025 Conference Paper

Domain-Specific Pruning of Large Mixture-of-Experts Models with Few-shot Demonstrations

  • Zican Dong
  • Han Peng
  • Peiyu Liu
  • Xin Zhao
  • Dong Wu
  • Feng Xiao
  • Zhifeng Wang

Mixture-of-Experts (MoE) models achieve a favorable trade-off between performance and inference efficiency by activating only a subset of experts. However, the memory overhead of storing all experts remains a major limitation, especially in large-scale MoE models such as DeepSeek-R1 (671B). In this study, we investigate domain specialization and expert redundancy in large-scale MoE models and uncover a consistent behavior we term~\emph{few-shot expert localization}, with only a few in-domain demonstrations, the model consistently activates a sparse and stable subset of experts on tasks within the same domain. Building on this observation, we propose a simple yet effective pruning framework, \textbf{EASY-EP}, that leverages a few domain-specific demonstrations to identify and retain only the most relevant experts. EASY-EP comprises two key components: \textbf{output-aware expert importance assessment} and \textbf{expert-level token contribution estimation}. The former evaluates the importance of each expert for the current token by considering the gating scores and L2 norm of the outputs of activated experts, while the latter assesses the contribution of tokens based on representation similarities before and after routed experts. Experiments on DeepSeek-R1 and DeepSeek-V3-0324 show that our method can achieve comparable performances and $2. 99\times$ throughput under the same memory budget as the full model, with only half the experts. Our code is available at https: //github. com/RUCAIBox/EASYEP.

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NeurIPS Conference 2024 Conference Paper

Exploring Context Window of Large Language Models via Decomposed Positional Vectors

  • Zican Dong
  • Junyi Li
  • Xin Men
  • Wayne X. Zhao
  • Bingning Wang
  • Zhen Tian
  • weipeng chen
  • Ji-Rong Wen

Transformer-based large language models (LLMs) typically have a limited context window, resulting in significant performance degradation when processing text beyond the length of the context window. Extensive studies have been proposed to extend the context window and achieve length extrapolation of LLMs, but there is still a lack of in-depth interpretation of these approaches. In this study, we explore the positional information within and beyond the context window for deciphering the underlying mechanism of LLMs. By using a mean-based decomposition method, we disentangle positional vectors from hidden states of LLMs and analyze their formation and effect on attention. Furthermore, when texts exceed the context window, we analyze the change of positional vectors in two settings, i. e. , direct extrapolation and context window extension. Based on our findings, we design two training-free context window extension methods, positional vector replacement and attention window extension. Experimental results show that our methods can effectively extend the context window length.

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