The field of large language models is moving towards more efficient and effective fine-tuning methods. Recent developments have focused on parameter-efficient fine-tuning (PEFT) techniques, which reduce the number of trainable parameters while maintaining or improving performance. These methods have shown promising results in various applications, including speech recognition and natural language understanding. Notably, techniques such as low-rank adaptation (LoRA) and its variants have been widely adopted, and new methods like structure-learnable adapters and vector-based random tensor networks have emerged. These innovations enable more flexible and efficient adaptation of large language models to downstream tasks, while also providing insights into the underlying mechanisms of these models.

Some noteworthy papers in this area include: LobRA, which introduces a framework for joint fine-tuning of LoRA adapters with heterogeneous resource usages and parallel configurations, achieving significant reductions in GPU seconds required for joint fine-tuning. SSVD, which presents a comprehensive integration and benchmarking of PEFT methods within ESPnet and introduces structured SVD-guided fine-tuning for robust domain adaptation. TeRA, which proposes a vector-based random tensor network for high-rank adaptation of large language models, achieving high-rank weight updates while retaining parameter efficiency. IPA, which introduces a feature-aware projection framework that explicitly preserves information in the reduced hidden space, improving performance over LoRA and DoRA. L1RA, which dynamically assigns rank to LoRA adapters during fine-tuning, optimizing resource utilization and achieving comparable or better performance than other LoRA variants. OLieRA, which introduces Lie group theory into LLM fine-tuning, preserving parameter geometry while enforcing orthogonality constraints to task subspaces, and achieving state-of-the-art results on the Standard CL benchmark.