2025
S'MoRE: Structural Mixture of Residual Experts for Parameter-Efficient LLM Fine-tuning
NeurIPS 2025poster
Fine-tuning pre-trained large language models (LLMs) presents a dual challenge of balancing parameter efficiency and model capacity. Existing methods like low-rank adaptations (LoRA) are efficient but lack flexibility, while Mixture-of-Experts (MoE) enhance model capacity at the cost of more & under…