从大语言模型构建大规模因果模型
Large Causal Models from Large Language Models
AI总结：
利用大语言模型构建大规模因果模型，通过DEMOCRITUS系统整合跨领域因果关系，实现因果陈述的组织与可视化。
摘要：
我们介绍了一种新的范式，用于构建大规模因果模型（LCMs），该范式利用了当今大语言模型（LLMs）中巨大的潜力。我们描述了我们正在进行的实验，使用了一个名为DEMOCRITUS（去中心化提取因果关系的 manifold 本体集成拓扑 universal 切片）的系统，旨在构建、组织和可视化从精心选择的文本查询中提取的跨不同领域的LCMs。DEMOCRITUS在方法上与传统的狭窄领域和假设中心的因果推断不同，后者通过产生数值数据的实验构建因果模型。高质量的LLM用于提出主题、生成因果问题并从各种领域中提取可能的因果陈述。技术挑战是将这些孤立、碎片化、可能模糊且可能冲突的因果主张编织成一个连贯的整体，将其转换为关系因果三元组并嵌入到LCM中。解决这一技术挑战需要发明新的范畴机器学习方法，我们只能在本文中简要总结，因为本文更侧重于构建DEMOCRITUS系统的技术方面。我们描述了DEMOCRITUS的实现流程，包括六个模块，分析了其计算成本概况，以确定当前在扩展系统以适应更大模型时的瓶颈。我们描述了在广泛领域使用DEMOCRITUS的结果，涵盖考古学、生物学、气候变化、经济学、医学和技术。我们讨论了当前DEMOCRITUS系统的局限性，并概述了扩展其能力的方向。
英文摘要：
We introduce a new paradigm for building large causal models (LCMs) that exploits the enormous potential latent in today's large language models (LLMs). We describe our ongoing experiments with an implemented system called DEMOCRITUS (Decentralized Extraction of Manifold Ontologies of Causal Relations Integrating Topos Universal Slices) aimed at building, organizing, and visualizing LCMs that span disparate domains extracted from carefully targeted textual queries to LLMs. DEMOCRITUS is methodologically distinct from traditional narrow domain and hypothesis centered causal inference that builds causal models from experiments that produce numerical data. A high-quality LLM is used to propose topics, generate causal questions, and extract plausible causal statements from a diverse range of domains. The technical challenge is then to take these isolated, fragmented, potentially ambiguous and possibly conflicting causal claims, and weave them into a coherent whole, converting them into relational causal triples and embedding them into a LCM. Addressing this technical challenge required inventing new categorical machine learning methods, which we can only briefly summarize in this paper, as it is focused more on the systems side of building DEMOCRITUS. We describe the implementation pipeline for DEMOCRITUS comprising of six modules, examine its computational cost profile to determine where the current bottlenecks in scaling the system to larger models. We describe the results of using DEMOCRITUS over a wide range of domains, spanning archaeology, biology, climate change, economics, medicine and technology. We discuss the limitations of the current DEMOCRITUS system, and outline directions for extending its capabilities.
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。
