Problem Solving

Problem-solving research currently focuses on enhancing the reasoning capabilities of large language models (LLMs) to tackle complex tasks, particularly those requiring multiple steps and handling incomplete or irrelevant information. This involves developing novel prompting techniques, such as those inspired by Bloom's Taxonomy or incorporating iterative thought processes and multi-agent systems, often utilizing transformer-based architectures and algorithms like Monte Carlo Tree Search. Improved LLMs for problem-solving have significant implications for various fields, including automated task assignment, educational tools, and enhancing efficiency in domains like geotechnical engineering and customer support.