Jun Yan
Jun Yan is a Research Scientist at Google, working on improving agentic capability and trustworthiness of Large Language Models (LLMs).
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Multi-turn Function-calling via Graph-based Execution and Translation
Kai-Wei Chang
Ke Jiang
Jindong Gu
Fan Yin
2025
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We propose a principled method to synthesize high-quality multi-turn function calling trajectories to align large language model (LLM)-based agents. We start with iteratively building function calling graph and defining node operations to increase its complexity. This enables us to construct reliable reference. Then, based on the synthesized function calling graph, we adopt back-and-forth translation to first construct multi-turn user queries and then, fill in the function arguments with information in the query. We sample positive trajectories that distill the function graph reference and negative trajectories that contrast with the positive trajectories in targeted loss patterns in multi-turn scenarios. Training with the positive trajectories with supervised fine-tuning and preference optimization against negative trajectories, we obtain 67.42 on BFCL and 71.7 on ToolQuery with an open-sourced model with 14B parameters, surpassing the performance of strong proprietary models like o1.
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