English(EN) DM-Call: Decision-Model-Guided Function Calling with Cumulative Verification Feedback

新的DM-Call框架提高了LLM函数调用的准确性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-07 21:29

研究人员推出了一种新颖的DM-Call框架，旨在提高大型语言模型（LLM）中函数调用的可靠性。该系统集成了本地模式验证和一个独立的决策模型验证过程，以确保适当函数及其参数的正确选择和正确性。DM-Call跨多次尝试累积反馈，结合数值概率和口头正确性标签，以指导迭代改进，而无需执行外部工具。在BFCL和API-Bank数据集上的评估表明，与直接生成方法相比，DM-Call显著提高了函数调用准确性，而计算成本仅略有增加。 AI

影响增强了LLM在执行外部工具时的可靠性，有可能提高代理能力和任务完成准确性。

排序理由该集群包含一篇详细介绍LLM函数调用新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.IR (Information Retrieval) 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Seunghyun Lee · 2026-11-07 21:29

DM-Call：基于决策模型的函数调用，并带有累积验证反馈

Reliable function calling requires Large Language Models (LLMs) to select appropriate functions and generate arguments consistent with user intent. However, syntactically valid calls may still be semantically incorrect due to inappropriate function selection or mismatched argumen…

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DM-Call：基于决策模型的函数调用，并带有累积验证反馈

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