The field of natural language processing is witnessing significant advancements in the development of chain-of-thought prompting methods for large language models. These methods aim to improve the transparency, interpretability, and trustworthiness of model outputs by structuring reasoning into step-by-step deductions. Recent studies have demonstrated the effectiveness of these approaches in reducing hallucinations, improving performance, and enhancing the overall decision-making capabilities of large language models. Notably, the integration of domain-specific expert knowledge and the use of causal reasoning alignment have shown promising results in refining reasoning chains and reducing biases. Furthermore, the development of frameworks for evaluating and refining reasoning chains has improved the interpretability and trustworthiness of model outputs. Overall, the field is moving towards the development of more transparent, reliable, and efficient large language models. Noteworthy papers include: FinCoT, which presents a structured chain-of-thought prompting approach that incorporates expert financial reasoning to guide the reasoning traces of large language models. ECCoT, a framework for enhancing effective cognition via chain of thought in large language models, which integrates topic-aware chain of thought generation and causal reasoning alignment to improve interpretability and reduce biases.