为大模型在垂直领域应用设计分层框架，解决实际部署中的可信与适配难题。
A Framework for Situating Innovations, Opportunities, and Challenges in Advancing Vertical Systems with Large AI Models
- 提出分层抽象框架，将大模型转化为符合真实需求的垂直系统
- 通过案例验证框架可指导创新定位与跨领域协作
- 强调跨学科沟通，推动建立通用术语体系
大型人工智能模型在标准化测试中表现出卓越性能，但在医疗、教育、法律等高风险垂直领域部署时，常暴露出对输入微小变化敏感、决策缺乏上下文依据、错误自信输出等问题。这些局限要求跨学科创新以匹配真实应用场景需求。本文提出一种分层抽象框架，旨在将大模型能力与用户需求对齐。通过多个案例研究，展示该框架如何帮助研究人员和实践者落地应用。框架不仅模块化了从大模型到实用垂直系统的转化流程，还揭示了各层级间的动态关系。最后，框架可指导研究者：(i)合理定位创新（如何时利用垂直洞察推动通用型创新）；(ii)发现被忽视的机会（如识别跨领域的共性问题，开发实用基础模型而非追逐基准）；(iii)促进跨学科交流（如为AI开发者、领域专家与人机交互学者建立共享术语体系）。
原文摘要 · Abstract (English)
Large artificial intelligence (AI) models have garnered significant attention for their remarkable, often "superhuman", performance on standardized benchmarks. However, when these models are deployed in high-stakes verticals such as healthcare, education, and law, they often reveal notable limitations. For instance, they exhibit brittleness to minor variations in input data, present contextually uninformed decisions in critical settings, and undermine user trust by confidently producing or reproducing inaccuracies. These challenges in applying large models necessitate cross-disciplinary innovations to align the models' capabilities with the needs of real-world applications. We introduce a framework that addresses this gap through a layer-wise abstraction of innovations aimed at meeting users' requirements with large models. Through multiple case studies, we illustrate how researchers and practitioners across various fields can operationalize this framework. Beyond modularizing the pipeline of transforming large models into useful "vertical systems", we also highlight the dynamism that exists within different layers of the framework. Finally, we discuss how our framework can guide researchers and practitioners to (i) optimally situate their innovations (e.g., when vertical-specific insights can empower broadly impactful vertical-agnostic innovations), (ii) uncover overlooked opportunities (e.g., spotting recurring problems across verticals to develop practically useful foundation models instead of chasing benchmarks), and (iii) facilitate cross-disciplinary communication of critical challenges (e.g., enabling a shared vocabulary for AI developers, domain experts, and human-computer interaction scholars). Project webpage: https://gaurav22verma.github.io/vertical-systems-with-large-ai-models/
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。