The field of large language models (LLMs) is moving towards improving their robustness and consistency. Recent research has focused on addressing the issue of order dependence in LLMs, where the order of input tokens can affect the model's predictions. Researchers are also exploring ways to improve the instruction-tuning process, which is crucial for enabling LLMs to solve real-world tasks. Another area of focus is on developing more interpretable and robust prompting methods, including soft prompts and adversarial instruction data mining. Additionally, there is a growing interest in enhancing the robustness of LLMs to perturbed instructions, which can significantly degrade their performance. Noteworthy papers include: Order Independence With Finetuning, which proposes a fine-tuning strategy to integrate set-based prompting into the training process, Building Instruction-Tuning Datasets from Human-Written Instructions with Open-Weight Large Language Models, which introduces a new approach to building instruction-tuning datasets from human-written instructions, Pay More Attention to the Robustness of Prompt for Instruction Data Mining, which proposes a framework for high-quality online instruction data mining, Towards Interpretable Soft Prompts, which creates a novel theoretical framework for evaluating the interpretability of trainable prompts, Enhancing LLM Robustness to Perturbed Instructions: An Empirical Study, which experiments with various techniques to enhance the robustness of LLMs to perturbed instructions.