Since the advent of Large Language Models a few years ago, they have often been considered the de facto solution for many AI problems. However, in addition to the many deficiencies of LLMs that prevent them from broad industry adoption, such as reliability, cost, and speed, there is a whole class of common real world problems that Large Language Models perform poorly on, namely, constraint satisfaction and optimization problems. These problems are ubiquitous and current solutions are highly specialized and expensive to implement. At Elemental Cognition, we developed our EC AI platform which takes a neuro-symbolic approach to solving constraint satisfaction and optimization problems. The platform employs, at its core, a precise and high performance logical reasoning engine, and leverages LLMs for knowledge acquisition and user interaction. This platform supports developers in specifying application logic in natural and concise language while generating application user interfaces to interact with users effectively. We evaluated LLMs against systems built on the EC AI platform in three domains and found the EC AI systems to significantly outperform LLMs on constructing valid and optimal solutions, on validating proposed solutions, and on repairing invalid solutions.

翻译：自数年前大型语言模型问世以来，它们常被视为众多人工智能问题的默认解决方案。然而，除了可靠性、成本和速度等阻碍其广泛行业应用的诸多缺陷外，还存在一类大型语言模型表现欠佳的常见现实问题——即约束满足与优化问题。这类问题普遍存在，但现有解决方案高度专业化且部署成本高昂。在Elemental Cognition公司，我们开发了EC AI平台，采用神经符号方法解决约束满足与优化问题。该平台核心采用精确且高性能的逻辑推理引擎，并利用大型语言模型实现知识获取与用户交互。该平台支持开发者用自然简洁的语言定义应用逻辑，同时生成用户交互界面以实现高效人机交互。我们在三个领域将大型语言模型与基于EC AI平台构建的系统进行对比评估，发现EC AI系统在构建有效最优解、验证候选方案及修复无效解方面均显著优于大型语言模型。