In real-world contexts such as medical diagnosis and business consulting, effective problem-solving often requires gathering relevant information through interactions and targeted questioning to pinpoint the root cause of a problem. However, Large Language Models (LLMs) often struggle to efficiently narrow down the search space, leading to either missing key information or asking redundant questions when guided by implicit methods like Chain-of-Thought (CoT). Some approaches employ external engineered systems to guide reasoning paths, but these methods may not fully utilize the inherent problem-solving capabilities of LLMs and often require multiple expensive API calls. This study explores how we can implicitly guide LLMs to enhance their interactive feature collection abilities within a single prompt. Instead of employing explicit search algorithms or step-by-step external guidance, we provide high-level guidelines that allow LLMs to dynamically adjust their strategies and iteratively refine their decision-making processes independently. Evaluations on synthetic 20-Questions games and real-world scenarios, including business and medical diagnosis cases, demonstrate that LLMs guided by these strategies perform more effective interactive feature collection, asking fewer and more strategic questions and achieving better problem-solving efficiency.

Recent works have successfully applied Large Language Models (LLMs) to function modeling tasks. However, the reasons behind this success remain unclear. In this work, we propose a new evaluation framework to comprehensively assess LLMs’ function modeling abilities. By adopting a Bayesian perspective of function modeling, we discover that LLMs are relatively weak in understanding patterns in raw data, but excel at utilizing prior knowledge about the domain to develop a strong understanding of the underlying function. Our findings offer new insights about the strengths and limitations of LLMs in the context of function modeling.