研究人员推出了一种名为SpecHop的新框架，旨在降低使用外部工具执行复杂多跳任务的大型语言模型的延迟。通过使用多线程的连续推测，SpecHop可以验证预测的观察结果并提交正确的执行路径，同时回滚错误的路径。这种方法旨在在显著减少信息密集型操作所需时间的同时保持准确性，实证结果显示在某些检索增强场景中延迟最多可降低40%。 AI
影响 降低了执行复杂多跳检索任务的LLM的延迟，可能加速信息密集型应用的运行。
排序理由 该集群包含一篇详细介绍新框架及其实证结果的学术论文。
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
arXiv:2605.21965v1 Announce Type: new Abstract: Large language models increasingly use external tools such as web search and document retrieval to solve information-intensive tasks. However, multi-hop tool use in complex tasks introduces substantial latency, since the model must …
Large language models increasingly use external tools such as web search and document retrieval to solve information-intensive tasks. However, multi-hop tool use in complex tasks introduces substantial latency, since the model must repeatedly wait for tool observations before con…