@inproceedings{yamanaka-etal-2025-flowchart,
title = "Flowchart-Based Decision Making with Large Language Models",
author = "Yamanaka, Yuuki and
Takahashi, Hiroshi and
Yamashita, Tomoya",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.766/",
doi = "10.18653/v1/2025.findings-acl.766",
pages = "14836--14842",
ISBN = "979-8-89176-256-5",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T Flowchart-Based Decision Making with Large Language Models
%A Yamanaka, Yuuki
%A Takahashi, Hiroshi
%A Yamashita, Tomoya
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F yamanaka-etal-2025-flowchart
%X 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.
%R 10.18653/v1/2025.findings-acl.766
%U https://aclanthology.org/2025.findings-acl.766/
%U https://doi.org/10.18653/v1/2025.findings-acl.766
%P 14836-14842
Markdown (Informal)
[Flowchart-Based Decision Making with Large Language Models](https://aclanthology.org/2025.findings-acl.766/) (Yamanaka et al., Findings 2025)
ACL