FREYR让大模型更高效调用工具，提升任务完成率
FREYR: A Framework for Recognizing and Executing Your Requests
- 将工具使用拆解为独立模块化步骤，提升灵活性
- 在游戏设计任务中表现优于Ollama API传统方法
- 适合需要动态调用新工具的开发场景
大语言模型在对话系统中表现出色，但通过调用可执行代码等工具可进一步提升响应准确性和专业领域处理能力。当前方法多依赖特定提示或微调模型以实现函数调用，存在适应未见工具能力弱、资源消耗高等问题。本文提出FREYR框架，将工具使用过程模块化分解为独立步骤。实验表明，该框架在视频游戏设计的真实任务测试中，性能优于Ollama API提供的传统工具调用方式。
原文摘要 · Abstract (English)
Large language models excel as conversational agents, but their capabilities can be further extended through tool usage, i.e.: executable code, to enhance response accuracy or address specialized domains. Current approaches to enable tool usage often rely on model-specific prompting or fine-tuning a model for function-calling instructions. Both approaches have notable limitations, including reduced adaptability to unseen tools and high resource requirements. This paper introduces FREYR, a streamlined framework that modularizes the tool usage process into separate steps. Through this decomposition, we show that FREYR achieves superior performance compared to conventional tool usage methods. We evaluate FREYR on a set of real-world test cases specific for video game design and compare it against traditional tool usage as provided by the Ollama API.
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