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Evaluating LLM Performance in TRIZ-Based System Forecasting: A Study Using 9-Windows

delete2026-01-01
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PRE
AI
M
Mélusine Caillard *
G
Giacomo Bersano
G
Gaston Opler
P
Pierre-Emmanuel Fayemi
DOI：10.1007/978-3-032-08847-5_8delete
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摘要

摘要

En 中文
大型语言模型(LLMs)在结构化预见任务中显示出潜力，但在连贯性和方法论一致性方面存在困难。本研究评估了七种LLMs在五个系统和三种提示类型下生成TRIZ系统操作工具(SOTs)的能力。在复杂或模糊的背景下，尤其是在趋势和超系统领域，性能下降。专家评审和语义指标表明，分解提示可以提高输出质量。研究发现，当前的LLMs需要人机协作和定制化提示来实现高级预见。
Keyword:
LLM
Data-driven TRIZ
Technology forecasting

期刊

W
WORLD CONFERENCE OF AI-POWERED INNOVATION AND TRIZ METHODOLOGY, TRAI 2025, PT I
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论文数:
22
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