DiaTool-DPO: Multi-Turn Direct Preference Optimization for
  Tool-Augmented Large Language Models
Authors: name: Sunghee Jung, name: Donghun Lee, name: Shinbok Lee, name: Gaeun Seo, name: Daniel Lee, name: Byeongil Ko, name: Junrae Cho, name: Kihyun Kim, name: Eunggyun Kim, name: Myeongcheol Shin

Tool-Augmented Larage Language Models (TA-LLMs) have shown promise in
real-world applications, but face challenges in handling incomplete queries and
out-of-scope requests. While existing approaches rely mainly on Supervised
Fine-Tuning with expert trajectories, we propose DiaTool-DPO, a novel method
that enhances TA-LLM's dialogue capabilities through Direct Preference
Optimization. We model TA-LLM interactions as a Markov Decision Process with 5
distinct dialogue states and categorize user queries into 3 types based on
their state transition trajectories. We automatically construct paired
trajectory datasets of correct and incorrect dialogue flows and introduce a
specialized objective loss for dialogue control. Our comprehensive evaluation
demonstrates that DiaTool-DPO approaches GPT-4o's performance (94.8% in
information gathering, 91% in tool call rejection) with substantial
improvements over baseline (44% and 9.6% respectively) while maintaining core
functionality. Our approach opens new possibilities for developing TA-LLMs that
can handle diverse real-world scenarios without requiring additional expert
demonstrations or human labeling.