利用设计结构矩阵优化大语言模型对话中的令牌使用
Optimizing Token Usage on Large Language Model Conversations Using the Design Structure Matrix
AI总结：
本文将工程设计中的设计结构矩阵引入大语言模型对话优化，通过聚类和排序组织对话，减少单次令牌使用量并分组上下文窗口，拓宽了令牌优化方法并开辟工程设计实践与大语言模型融合的新途径。
摘要：
随着大语言模型在许多领域和任务中变得无处不在，需要减少令牌使用，以克服诸如短上下文窗口、有限的输出大小以及与令牌输入和生成相关的成本等挑战，尤其是在API服务的大语言模型中。本工作将工程设计学科中的设计结构矩阵引入大语言模型对话优化。应用于一个大语言模型对话涉及航天器及其子系统设计的用例中，设计结构矩阵凭借其聚类和排序等分析工具，证明了它是一种有效的工具，可以组织对话，最小化一次发送给大语言模型或从大语言模型检索的令牌数量，并将可分配到不同上下文窗口的块进行分组。因此，本工作拓宽了当前令牌使用优化的方法集，并为将工程设计实践集成到大语言模型中开辟了新途径。
英文摘要：
As Large Language Models become ubiquitous in many sectors and tasks, there is a need to reduce token usage, overcoming challenges such as short context windows, limited output sizes, and costs associated with token intake and generation, especially in API-served LLMs. This work brings the Design Structure Matrix from the engineering design discipline into LLM conversation optimization. Applied to a use case in which the LLM conversation is about the design of a spacecraft and its subsystems, the DSM, with its analysis tools such as clustering and sequencing, demonstrates being an effective tool to organize the conversation, minimizing the number of tokens sent to or retrieved from the LLM at once, as well as grouping chunks that can be allocated to different context windows. Hence, this work broadens the current set of methodologies for token usage optimization and opens new avenues for the integration of engineering design practices into LLMs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
