# HDMoLE: Mixture of LoRA Experts with Hierarchical Routing and Dynamic Thresholds for Fine-Tuning LLM-based ASR Models

arXiv: https://arxiv.org/abs/2409.19878 · HTML: https://mlsanity.com/paper/2409.19878/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal MoE models, one of 12 directions in Mixture-of-experts.
- First posted on arXiv 30 Sep 2024.

## Placed in

- Mixture-of-experts / [Multimodal MoE models](https://mlsanity.com/moe/multimodal-moe-models/)

## Built on by (most cited first, 9 of 9)

- [Bench-CoE: a Framework for Collaboration of Experts from Benchmark](https://mlsanity.com/paper/2412.04167/) (2024)
- [On the Role of Encoder Depth: Pruning Whisper and LoRA Fine-Tuning in SLAM-ASR](https://mlsanity.com/paper/2603.27981/) (2026)
- [AC-LoRA: (Almost) Training-Free Access Control-Aware Multi-Modal LLMs](https://mlsanity.com/paper/2505.11557/) (2025)
- [ILT-Iterative LoRA Training through Focus-Feedback-Fix for Multilingual Speech Recognition](https://mlsanity.com/paper/2507.08477/) (2025)
- [GLAD: Global-Local Aware Dynamic Mixture-of-Experts for Multi-Talker ASR](https://mlsanity.com/paper/2509.13093/) (2025)
- [A Language-Agnostic Hierarchical LoRA-MoE Architecture for CTC-based Multilingual ASR](https://mlsanity.com/paper/2601.00557/) (2026)
- [Dynamic Multi-Expert Projectors with Stabilized Routing for Multilingual Speech Recognition](https://mlsanity.com/paper/2601.19451/) (2026)
- [Entropy-Aware Domain-Routed Mixture-of-Experts Speech-LLM Framework: A Case Study of Multi-Domain Child-Adult ASR](https://mlsanity.com/paper/2606.10454/) (2026)
- [MoLGE: Mixture of Language Group Experts for Efficient Scaling of Massively Multilingual Speech Recognition](https://mlsanity.com/paper/2607.24030/) (2026)

## Abstract (arXiv)

Recent advancements in integrating Large Language Models (LLM) with automatic speech recognition (ASR) have performed remarkably in general domains. While supervised fine-tuning (SFT) of all model parameters is often employed to adapt pre-trained LLM-based ASR models to specific domains, it imposes high computational costs and notably reduces their performance in general domains. In this paper, we propose a novel parameter-efficient multi-domain fine-tuning method for adapting pre-trained LLM-based ASR models to multi-accent domains without catastrophic forgetting named textitHDMoLE, which leverages hierarchical routing and dynamic thresholds based on combining low-rank adaptation (LoRA) with the mixer of experts (MoE) and can be generalized to any linear layer. Hierarchical routing establishes a clear correspondence between LoRA experts and accent domains, improving cross-domain collaboration among the LoRA experts. Unlike the static Top-K strategy for activating LoRA experts, dynamic thresholds can adaptively activate varying numbers of LoRA experts at each MoE layer. Experiments on the multi-accent and standard Mandarin datasets demonstrate the efficacy of HDMoLE. Applying HDMoLE to an LLM-based ASR model projector module achieves similar performance to full fine-tuning in the target multi-accent domains while using only 9.6% of the trainable parameters required for full fine-tuning and minimal degradation in the source general domain.