Bo Yuan

Strategic planning is critical for multi-step reasoning, yet compact Language Language Models (LLMs) often lack the capacity to formulate global strategies, leading to error propagation in long-horizon tasks. Our analysis reveals that LLMs possess latent reasoning capabilities that can be unlocke...

Recent studies integrate Low-Rank Adaptation (LoRA) and Mixture-of-Experts (MoE) to further enhance the performance of parameter-efficient fine-tuning (PEFT) methods in Large Language Model (LLM) applications. Existing methods employ homogeneous MoE-LoRA architectures composed of LoRA experts wit...

Learning with Noisy Labels (LNL) is a challenge where the collected training set can contain incorrect or corrupted labels. Most existing solutions distinguish clean samples from noisy samples and query human experts on noisy samples for denoising. However, these solutions often operate under the...