從零訓練混合專家模型的巨大成本，促使將預訓練稠密模型轉換為稀疏混合專家模型的相關研究逐漸受到重視。然而，現有的稠密至稀疏混合專家模型建構方法在「初始專家多樣性」與「知識繼承」之間面臨根本性的權衡，且仍需要大量持續預訓練才能取得卓越的模型性能。對此，本論文提出「基於資料驅動低秩分解的高效混合專家模型」（Mixture of Efficient Data-driven Low-rank eXperts, MEDLX），這是一個旨在克服前述現有限制的混合專家模型建構框架。MEDLX 將資料驅動模型壓縮方法作為專家建構演算法，利用其能在注入特定資料特徵同時保留預訓練知識的優勢，從而取得兼具初始多樣性與知識的專家。本論文應用「截斷感知資料白化」壓縮方法，使用不同的校準資料集壓縮基礎模型的前饋網路權重以建構專家。實驗結果顯示，這些專家不僅具備顯著的初始功能多樣性，同時也從基礎模型繼承了大量的預訓練知識。受益於此，MEDLX 能搭配參數高效的訓練策略，在僅訓練門控網路與低秩適應適配器的前提下，減少超過 85% 的訓練參數量，同時取得優秀的模型性能。在相同且受限的計算預算下，MEDLX 的性能顯著優於現有的稠密至稀疏混合專家模型建構方法。此外，我們驗證了 MEDLX 建構的專家能發展出清晰且可解釋的功能性分工，其行為與過去研究對 Transformer 層級功能的既有理解相符。總體而言，MEDLX 開創了一個兼具效益與效率，基於資料驅動模型壓縮方法建構混合專家模型的方向，有效緩解了過去稠密至稀疏混合專家模型建構方法所面臨的挑戰。

The high cost of training mixture of experts (MoE) models from scratch has led to increased attention on converting pre-trained dense models into sparse MoE models. However, existing dense-to-sparse MoE construction methods face a fundamental trade-off between initial expert diversity and knowledge inheritance, and still require extensive continual pre-training to achieve excellent model performance. To this end, we propose the Mixture of Efficient Data-driven Low-rank eXperts (MEDLX), an MoE construction framework aimed at overcoming these limitations. MEDLX uses data-driven model compression as an expert construction algorithm to obtain experts with initial diversity and pre-trained knowledge. Our method is based on the "truncation-aware data whitening" compression technique, which we apply to the feed-forward network weights of a base model using different calibration datasets to construct the experts. Experimental results show that these experts not only possess significant initial functional diversity but also inherit substantial pre-trained knowledge from the base model. Benefiting from this, MEDLX achieves excellent performance with a parameter-efficient training strategy that reduces trainable parameters by over 85%, as only the gating network and low-rank adaptation adapters require training. Under identical and limited computational budgets, MEDLX's performance significantly surpasses existing dense-to-sparse MoE construction methods. Furthermore, we verify that the experts constructed by MEDLX develop clear and interpretable functional specializations, with behaviors that align with the established understanding of Transformer layer functionalities. Overall, MEDLX pioneers an effective and efficient direction for constructing MoE models via data-driven model compression, mitigating the challenges faced by previous dense-to-sparse MoE construction methods.

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