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MoxE: Mixture of xLSTM Experts with Entropy-Aware Routing for Efficient Language Modeling

Abdoul Majid O. Thiombiano, Brahim Hnich, Ali Ben Mrad, Mohamed Wiem Mkaouer

TL;DR

MoxE addresses the efficiency bottlenecks of large language models by integrating xLSTM units with a sparsely gated Mixture of Experts, hinged on an entropy-aware routing mechanism. By routing tokens to heterogeneous expert types (mLSTM and sLSTM) according to token difficulty, and by employing auxiliary losses to balance usage and stabilize training, MoxE achieves improved efficiency and performance relative to attention-based baselines. Theoretical analyses establish linear training complexity and practical reductions in compute, while experiments and ablations demonstrate the importance of heterogeneous experts, entropy-based routing, and balancing losses. This work offers a compelling recurrent MoE alternative to transformer-centric scaling, with potential implications for scalable, efficient language modeling in diverse settings.

Abstract

This paper introduces MoxE, a novel architecture that synergistically combines the Extended Long Short-Term Memory (xLSTM) with the Mixture of Experts (MoE) framework to address critical scalability and efficiency challenges in large language models (LLMs). The proposed method effectively leverages xLSTM's innovative memory structures while strategically introducing sparsity through MoE to substantially reduce computational overhead. At the heart of our approach is a novel entropy-based routing mechanism, designed to dynamically route tokens to specialized experts, thereby ensuring efficient and balanced resource utilization. This entropy awareness enables the architecture to effectively manage both rare and common tokens, with mLSTM blocks being favored to handle rare tokens. To further enhance generalization, we introduce a suite of auxiliary losses, including entropy-based and group-wise balancing losses, ensuring robust performance and efficient training. Theoretical analysis and empirical evaluations rigorously demonstrate that MoxE achieves significant efficiency gains and enhanced effectiveness compared to existing approaches, marking a notable advancement in scalable LLM architectures.

MoxE: Mixture of xLSTM Experts with Entropy-Aware Routing for Efficient Language Modeling

TL;DR

MoxE addresses the efficiency bottlenecks of large language models by integrating xLSTM units with a sparsely gated Mixture of Experts, hinged on an entropy-aware routing mechanism. By routing tokens to heterogeneous expert types (mLSTM and sLSTM) according to token difficulty, and by employing auxiliary losses to balance usage and stabilize training, MoxE achieves improved efficiency and performance relative to attention-based baselines. Theoretical analyses establish linear training complexity and practical reductions in compute, while experiments and ablations demonstrate the importance of heterogeneous experts, entropy-based routing, and balancing losses. This work offers a compelling recurrent MoE alternative to transformer-centric scaling, with potential implications for scalable, efficient language modeling in diverse settings.

Abstract

This paper introduces MoxE, a novel architecture that synergistically combines the Extended Long Short-Term Memory (xLSTM) with the Mixture of Experts (MoE) framework to address critical scalability and efficiency challenges in large language models (LLMs). The proposed method effectively leverages xLSTM's innovative memory structures while strategically introducing sparsity through MoE to substantially reduce computational overhead. At the heart of our approach is a novel entropy-based routing mechanism, designed to dynamically route tokens to specialized experts, thereby ensuring efficient and balanced resource utilization. This entropy awareness enables the architecture to effectively manage both rare and common tokens, with mLSTM blocks being favored to handle rare tokens. To further enhance generalization, we introduce a suite of auxiliary losses, including entropy-based and group-wise balancing losses, ensuring robust performance and efficient training. Theoretical analysis and empirical evaluations rigorously demonstrate that MoxE achieves significant efficiency gains and enhanced effectiveness compared to existing approaches, marking a notable advancement in scalable LLM architectures.
Paper Structure (26 sections, 25 equations, 8 figures, 2 tables)

This paper contains 26 sections, 25 equations, 8 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Extended Long Short-Term Memory (xLSTM)
  4. Mixture of Experts (MoE)
  5. Sparsely-Gated MoE in Large Language Models
  6. Recent Advancements in MoE Routing
  7. MoxE Architecture
  8. Overview
  9. Difficulty Assessment and Router Biasing
  10. Loss Functions and Balancing Strategies
  11. Auxiliary Difficulty Loss
  12. Group-Wise Auxiliary Loss
  13. Router Z-Loss
  14. Load Balancing Auxiliary Loss
  15. Total Loss
  16. ...and 11 more sections

Figures (8)

  • Figure 1: The MoxE architecture with an xLSTM sequence mixer (composed with one sLSTM unit and one mLSTM unit).
  • Figure 2: A side-by-side comparison of an attention-based MoE model (on the left) and a MoxE model (on the right).
  • Figure 3: Cross-entropy loss and evaluation of baseline models on Fineweb-Edu
  • Figure 4: Baseline models' average perplexity on Lambada OpenAI
  • Figure 5: Training loss and evaluation on Fineweb-Edu after our conducted ablation studies
  • ...and 3 more figures