LoRALib: A Standardized Benchmark for Evaluating LoRA-MoE Methods

2025-09-14

AI Summary

Existing LoRA-MoE studies suffer from inconsistent model architectures, datasets, hyperparameters, and evaluation protocols, hindering fair comparative analysis. Method: We introduce LoRALib, the first systematic benchmark for LoRA-MoE evaluation—integrating 17 model architectures, 680 LoRA modules across 40 downstream tasks, with standardized data formats, fine-tuning pipelines, and hyperparameter configurations; built upon OpenCompass to ensure reproducible large-scale assessment. Contribution/Results: Our key finding is that a task-correlation-aware LoRA selection mechanism significantly improves MoE performance and cross-task generalization. Extensive experiments establish LoRA-MoE as the current state-of-the-art paradigm. All code, data, and trained models are publicly released.

Technology Category

Machine Learning: Mixture of Experts (MoE)Natural Language Processing: (Large) Language ModelsComputer Vision: Large Vision Models

Application Category

Search and Retrieval-Augmented AI: Web evaluation methodologies and metricsEconomics, Online Markets and Human Computation: Cost models of using LLMs in production systemsUser Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

As a parameter efficient fine-tuning (PEFT) method, low-rank adaptation (LoRA) can save significant costs in storage and computing, but its strong adaptability to a single task is often accompanied by insufficient cross-task generalization capabilities. To improve this, existing work combines LoRA with mixture-of-experts (MoE) to enhance the model's adaptability through expert modules and routing mechanisms. However, existing LoRA-MoE methods lack unified standards in models, datasets, hyperparameters, and evaluation methods, making it difficult to conduct fair comparisons between different methods. To this end, we proposed a unified benchmark named LoRALib. Specifically, we standardized datasets from $40$ downstream tasks into a unified format, fine-tuned them using the same hyperparameters and obtained $680$ LoRA modules across $17$ model architectures. Based on this LoRA library, we conduct large-scale experiments on $3$ representative LoRA-MoE methods and different LoRA selection mechanisms using the open-sourced testing tool OpenCompass. Extensive experiments show that LoRAMoE performs best, and that prioritizing LoRAs relevant to the target task can further improve the performance of MoE. We hope these findings will inspire future work. Our datasets and LoRA library are available at https://huggingface.co/datasets/YaoLuzjut/LoRAOcean_dataset and https://huggingface.co/YaoLuzjut/models.

Problem

• Standardizing evaluation of LoRA-MoE methods lacking unified benchmarks
• Addressing insufficient cross-task generalization in LoRA fine-tuning
• Enabling fair comparison across models, datasets, and hyperparameters

Innovation

• Standardized datasets from 40 downstream tasks
• Fine-tuned 680 LoRA modules across 17 models
• Benchmarked 3 LoRA-MoE methods with OpenCompass