Flowchart-Based Decision Making with Large Language Models (2025)

Abstract: Large language models (LLMs) are widely used for conversational systems, but they face significant challenges in interpretability of dialogue flow and reproducibility of expert knowledge. To address this, we propose a novel method that extracts flowcharts from dialogue data and incorporates them into LLMs. This approach not only makes the decision-making process more interpretable through visual representation, but also ensures the reproducibility of expert knowledge by explicitly modeling structured reasoning flows. By evaluating on dialogue datasets, we demonstrate that our method effectively reconstructs expert decision-making paths with high precision and recall scores. These findings underscore the potential of flowchart-based decision making to bridge the gap between flexibility and structured reasoning, making chatbot systems more interpretable for developers and end-users.

## introduction the rapid advancement of large language models (llms) has enabled their widespread adoption in real-world applications, including customer support, medical diagnosis, and security incident response @xcite @xcite @xcite . in particular, conversational agent systems use llms for more flexible and adaptive interactions compared to traditional rulebased approaches @xcite @xcite . however, two fundamental challenges remain: interpretability of dialogue flows and reproducibility of expert knowledge. first, llm-driven dialogues lack interpretability, making it difficult for system administrators to control and manage interactions. while retrievalaugmented generation (rag) and fine-tuning can improve llm knowledge, the decision-making process remains opaque @xcite @xcite @xcite . second, the reproducibility of expert knowledge is critical in high-stakes domains such as medical diagnosis and troubleshooting. experts rely on structured decision-making processes, but existing llm-based conversational agent systems lack explicit modelling of these reasoning flows, leading to inconsistencies. moreover, expert knowledge is constantly evolving, making it challenging to keep dialogue systems up-to-date with the latest domain expertise @xcite @xcite . to address these challenges, we propose a novel method that extracts flowcharts from dialogue data and incorporates them into llms. this approach not only makes the decision-making process more interpretable through visual representation, but also ensures the reproducibility of expert knowledge by explicitly modeling structured reasoning flows. the flowchart-based representation allows system administrators to easily monitor, update, and maintain the dialogue system while preserving the expert decision-making patterns embedded in the training data. our method consists of three main steps: extracting key decision points from dialogues by identifying expert questions and suggestions, structuring them into logical decision flows, and constructing a refined flowchart by aggregating these flows. ## proposed method flowcharts are effective tools for visualizing complex processes @xcite . our method extract decision-making flows as flowcharts from dialogue data between expert operators and users, as illustrated in figure 1 . this approach is particularly effective in high-stakes applications such as disease diagnosis and troubleshooting, where operators identify and resolve users' issues. our method consists of the following three steps: (1) question and suggestion extraction, (2) dialogue flow extraction, and (3) flowchart construction. ## conclusion we introduced a method for extracting and visualizing expert decision flows from dialogue data using flowcharts, addressing interpretability and reproducibility challenges in conversational agent systems. utilizing the flodial dataset, our approach effectively captures comprehensive decision paths, as evidenced by high recall scores. these findings underscore the potential of flowchart-based decision making to enhance the transparency and reliability of chatbot systems, making them more interpretable for developers and end-users.