FunReason: Enhancing Large Language Models' Function Calling via Self-Refinement Multiscale Loss and Automated Data Refinement
Journal:
arXiv
Published Date:
May 26, 2025
Abstract
The integration of large language models (LLMs) with function calling has
emerged as a crucial capability for enhancing their practical utility in
real-world applications. However, effectively combining reasoning processes
with accurate function execution remains a significant challenge. Traditional
training approaches often struggle to balance the detailed reasoning steps with
the precision of function calls, leading to suboptimal performance. To address
these limitations, we introduce FunReason, a novel framework that enhances
LLMs' function calling capabilities through an automated data refinement
strategy and a Self-Refinement Multiscale Loss (SRML) approach. FunReason
leverages LLMs' natural reasoning abilities to generate high-quality training
examples, focusing on query parseability, reasoning coherence, and function
call precision. The SRML approach dynamically balances the contribution of
reasoning processes and function call accuracy during training, addressing the
inherent trade-off between these two critical aspects. FunReason achieves
performance comparable to GPT-4o while effectively mitigating catastrophic
forgetting during fine-tuning. FunReason provides a comprehensive solution for
enhancing LLMs' function calling capabilities by introducing a balanced
training methodology and a data refinement pipeline. For code and dataset,
please refer to our repository at GitHub
https://github.com/BingguangHao/FunReason
