Flowchart-Based Decision Making with Large Language Models

ACL ARR 2025 February Submission1084 Authors

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.

Paper Type: Short

Research Area: Dialogue and Interactive Systems

Research Area Keywords: task-oriented, dialogue state tracking, conversational modeling

Contribution Types: NLP engineering experiment

Languages Studied: English

Submission Number: 1084