The recent advancements in the research area of Large Language Models (LLMs) and their applications have shown significant progress in enhancing interpretability, robustness, and usability. A notable trend is the integration of LLMs into various domains to provide human-readable explanations and to assist in decision-making processes. This is evident in the development of tools like Explingo, which leverages LLMs to transform machine learning explanations into narrative formats, thereby improving their accessibility and usability. Another direction is the exploration of novel frameworks for enhancing the resistance of LLMs to adversarial prompts, as seen in the recursive framework proposed to simplify and detect malicious inputs. Additionally, there is a growing interest in the interpretability of model activations through the use of Sparse Autoencoders, with innovations like BatchTopK SAEs that adaptively allocate latents for improved reconstruction. The field is also witnessing advancements in visual analytics for specialized domains such as jurisprudence, where interactive visualization is being used to articulate tacit domain knowledge. Furthermore, the concept of using LLMs as visual explainers is emerging, offering a new approach to interpreting vision models through linguistic explanations. Overall, these developments highlight a shift towards more transparent, robust, and interactive applications of LLMs across diverse fields.