AdaMoLE is a novel method for fine-tuning large language models (LLMs) through an Adaptive Mixture of Low-Rank Adaptation (LoRA) Experts. Unlike conventional methods that use a static top-k strategy for activating experts, AdaMoLE dynamically adjusts the activation threshold using a dedicated threshold network, adaptively responding to the varying complexities of different tasks. By replacing a single LoRA in a layer with multiple LoRA experts and integrating a gating function with the threshold mechanism, AdaMoLE effectively selects and activates the most appropriate experts based on the input context. Extensive evaluations across various commonsense reasoning and natural language processing tasks show that AdaMoLE outperforms baseline methods, demonstrating the effectiveness of its adaptive expert selection. The experimental results confirm AdaMoLE as a robust approach for enhancing LLMs and suggest valuable directions for future research in adaptive expert selection mechanisms. AdaMoLE integrates Low-Rank Adaptation (LoRA) with an adaptive Mixture of Experts (MoE) framework, featuring a dynamic threshold network that facilitates context-sensitive expert activation. This innovation allows AdaMoLE to fine-tune its activation of experts based on the context of the input, providing a more refined and context-aware approach to model adaptation. The main contributions of AdaMoLE include its advanced integration of LoRA and an adaptive MoE framework, its superior adaptability and performance across various tasks, and insights into the model's operational dynamics through threshold sensitivity and expert activation analyses. The integration of MoE and LoRA has been a prominent area of research in enhancing LLMs. Recent studies have explored the combination of these techniques to improve model performance. AdaMoLE introduces a dynamic thresholding mechanism, offering a more context-responsive and flexible strategy for expert activation, optimizing the fine-tuning process across various tasks. This approach allows AdaMoLE to dynamically adjust the number of engaged experts based on the input context, enhancing the MoE layer's adaptability and effectiveness. In experiments, AdaMoLE was evaluated on various benchmarks, including commonsense reasoning and NLP tasks. The results showed that AdaMoLE outperformed traditional baselines, demonstrating its effectiveness in handling a variety of datasets. The performance of AdaMoLE was measured using accuracy as the primary metric, with the model achieving notable improvements across different tasks. The analysis of threshold sensitivity and expert activation behavior revealed that AdaMoLE's dynamic thresholding mechanism plays a crucial role in balancing computational efficiency with expert engagement across diverse tasks. The results of the experiments indicate that AdaMoLE's dynamic thresholding mechanism significantly enhances the model's performance. The model's ability to adaptively select and activate experts based on the input context allows it to effectively handle a wide range of tasks and contents with varying complexity. The findings highlight the potential of AdaMoLE as a versatile tool for fine-tuning LLMs, demonstrating its broad applicability and effectiveness in enhancing model performance. The study also identifies limitations, such as the computational overheadAdaMoLE is a novel method for fine-tuning large language models (LLMs) through an Adaptive Mixture of Low-Rank Adaptation (LoRA) Experts. Unlike conventional methods that use a static top-k strategy for activating experts, AdaMoLE dynamically adjusts the activation threshold using a dedicated threshold network, adaptively responding to the varying complexities of different tasks. By replacing a single LoRA in a layer with multiple LoRA experts and integrating a gating function with the threshold mechanism, AdaMoLE effectively selects and activates the most appropriate experts based on the input context. Extensive evaluations across various commonsense reasoning and natural language processing tasks show that AdaMoLE outperforms baseline methods, demonstrating the effectiveness of its adaptive expert selection. The experimental results confirm AdaMoLE as a robust approach for enhancing LLMs and suggest valuable directions for future research in adaptive expert selection mechanisms. AdaMoLE integrates Low-Rank Adaptation (LoRA) with an adaptive Mixture of Experts (MoE) framework, featuring a dynamic threshold network that facilitates context-sensitive expert activation. This innovation allows AdaMoLE to fine-tune its activation of experts based on the context of the input, providing a more refined and context-aware approach to model adaptation. The main contributions of AdaMoLE include its advanced integration of LoRA and an adaptive MoE framework, its superior adaptability and performance across various tasks, and insights into the model's operational dynamics through threshold sensitivity and expert activation analyses. The integration of MoE and LoRA has been a prominent area of research in enhancing LLMs. Recent studies have explored the combination of these techniques to improve model performance. AdaMoLE introduces a dynamic thresholding mechanism, offering a more context-responsive and flexible strategy for expert activation, optimizing the fine-tuning process across various tasks. This approach allows AdaMoLE to dynamically adjust the number of engaged experts based on the input context, enhancing the MoE layer's adaptability and effectiveness. In experiments, AdaMoLE was evaluated on various benchmarks, including commonsense reasoning and NLP tasks. The results showed that AdaMoLE outperformed traditional baselines, demonstrating its effectiveness in handling a variety of datasets. The performance of AdaMoLE was measured using accuracy as the primary metric, with the model achieving notable improvements across different tasks. The analysis of threshold sensitivity and expert activation behavior revealed that AdaMoLE's dynamic thresholding mechanism plays a crucial role in balancing computational efficiency with expert engagement across diverse tasks. The results of the experiments indicate that AdaMoLE's dynamic thresholding mechanism significantly enhances the model's performance. The model's ability to adaptively select and activate experts based on the input context allows it to effectively handle a wide range of tasks and contents with varying complexity. The findings highlight the potential of AdaMoLE as a versatile tool for fine-tuning LLMs, demonstrating its broad applicability and effectiveness in enhancing model performance. The study also identifies limitations, such as the computational overhead