The field of natural language processing (NLP) is witnessing significant advancements, driven by the development of more sophisticated AI-language models. These models are not only improving in terms of performance and architecture but also in their ability to align with human values and moral preferences. A key area of focus is the evaluation and improvement of large language models' (LLMs) moral tendencies and implicit biases. Research is also exploring the creation of high-quality, large-scale datasets that can help in aligning LLMs with human preferences. Noteworthy papers include: From ChatGPT to DeepSeek AI, which presents a comprehensive analysis of the evolution of AI-language models. COIG-P introduces a high-quality, large-scale Chinese preference dataset that significantly outperforms other datasets. From Stability to Inconsistency presents a study on the moral preferences in LLMs, revealing a lack of consistency in state-of-the-art models.