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【第96期】AsyncLM：异步LLM函数调用
Seventy3: 用NotebookLM将论文生成播客，让大家跟着AI一起进步。
今天的主题是：Asynchronous LLM Function CallingSummary
This research paper introduces AsyncLM, a system designed to enhance the efficiency of Large Language Models (LLMs) by enabling asynchronous function calls. Unlike current synchronous methods where LLMs block while awaiting function execution, AsyncLM allows concurrent operation, significantly reducing task completion latency. This is achieved through an interrupt mechanism that notifies the LLM when functions complete, along with a novel domain-specific language (CML) and a fine-tuning strategy to handle this asynchronous interaction. The paper presents empirical evidence demonstrating substantial latency reduction and maintains accuracy, even suggesting extensions for novel human-LLM or LLM-LLM interactions.
这篇研究论文介绍了 AsyncLM，一种通过实现异步函数调用来提升大型语言模型（LLMs）效率的系统。与当前同步方法中 LLM 等待函数执行完成而阻塞的情况不同，AsyncLM 允许并发操作，显著降低了任务完成的延迟。该系统通过中断机制实现，当函数执行完成时通知 LLM，同时引入了一种新颖的领域特定语言（CML）以及用于处理异步交互的微调策略。论文提供了实证证据，显示 AsyncLM 在显著减少延迟的同时保持了高精度，并提出了其在全新的人类-LLM 或 LLM-LLM 交互场景中的扩展潜力。
原文链接：https://arxiv.org/abs/2412.07017
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By 任雨山Seventy3: 用NotebookLM将论文生成播客，让大家跟着AI一起进步。
今天的主题是：Asynchronous LLM Function CallingSummary
This research paper introduces AsyncLM, a system designed to enhance the efficiency of Large Language Models (LLMs) by enabling asynchronous function calls. Unlike current synchronous methods where LLMs block while awaiting function execution, AsyncLM allows concurrent operation, significantly reducing task completion latency. This is achieved through an interrupt mechanism that notifies the LLM when functions complete, along with a novel domain-specific language (CML) and a fine-tuning strategy to handle this asynchronous interaction. The paper presents empirical evidence demonstrating substantial latency reduction and maintains accuracy, even suggesting extensions for novel human-LLM or LLM-LLM interactions.
这篇研究论文介绍了 AsyncLM，一种通过实现异步函数调用来提升大型语言模型（LLMs）效率的系统。与当前同步方法中 LLM 等待函数执行完成而阻塞的情况不同，AsyncLM 允许并发操作，显著降低了任务完成的延迟。该系统通过中断机制实现，当函数执行完成时通知 LLM，同时引入了一种新颖的领域特定语言（CML）以及用于处理异步交互的微调策略。论文提供了实证证据，显示 AsyncLM 在显著减少延迟的同时保持了高精度，并提出了其在全新的人类-LLM 或 LLM-LLM 交互场景中的扩展潜力。
原文链接：https://arxiv.org/abs/2412.07017