用生成模型把理论变成交互式度量,助力跨领域问题定位
GEMS: Generative Expert Metric System through Iterative Prompt Priming
- 基于神经活动启发的提示工程框架,实现理论到度量的自动转化
- 在软件仓库数据上验证,可生成上下文感知的度量指标
- 适合需将抽象理论转化为具体指标的研究者与实践者
在各领域中,度量与评估是识别挑战、支持决策和化解冲突的基础。尽管当前信息爆炸,单一专家难以跨学科处理数据,非专家也常难以设计有效度量或把理论转化为适配场景的指标。本技术报告聚焦大型企业中的软件社区,研究如何利用度量作为代理,定位组织内知识传递对象。我们提出一种受神经活动启发的提示工程框架,证明生成模型可提取并总结理论,执行基础推理,从而将概念转化为基于软件仓库数据的上下文感知度量。虽以软件社区为案例,但该框架具备跨领域扩展潜力,展示由专家理论驱动的度量系统,有助于优先处理复杂挑战。
原文摘要 · Abstract (English)
Across domains, metrics and measurements are fundamental to identifying challenges, informing decisions, and resolving conflicts. Despite the abundance of data available in this information age, not only can it be challenging for a single expert to work across multi-disciplinary data, but non-experts can also find it unintuitive to create effective measures or transform theories into context-specific metrics that are chosen appropriately. This technical report addresses this challenge by examining software communities within large software corporations, where different measures are used as proxies to locate counterparts within the organization to transfer tacit knowledge. We propose a prompt-engineering framework inspired by neural activities, demonstrating that generative models can extract and summarize theories and perform basic reasoning, thereby transforming concepts into context-aware metrics to support software communities given software repository data. While this research zoomed in on software communities, we believe the framework's applicability extends across various fields, showcasing expert-theory-inspired metrics that aid in triaging complex challenges.
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