用工程设计矩阵优化大模型对话，减少令牌消耗。
Optimizing Token Usage on Large Language Model Conversations Using the Design Structure Matrix
- 引入设计结构矩阵组织对话流程，自动分组与排序信息块。
- 在航天器设计对话中降低单次交互令牌数，提升上下文利用效率。
- 适合需要高效对话管理的工程类AI应用，如系统设计辅助。
随着大语言模型在多个领域广泛应用，降低令牌使用成为关键挑战，尤其在上下文窗口受限、输出长度有限及API调用成本较高的场景下。本文将工程设计领域的设计结构矩阵（DSM）引入大模型对话优化，应用于航天器及其子系统的设计对话场景。通过聚类与排序等分析工具，DSM有效组织对话内容，减少每次发送或检索到大模型的令牌数量，并可将信息块分配至不同上下文窗口。该方法拓展了现有令牌优化手段，为工程设计方法与大模型融合提供了新路径。
原文摘要 · Abstract (English)
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.
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