连接自然语言与ASP：一种使用LLM和AMR解析的混合方法
Bridging Natural Language and ASP: A Hybrid Approach Using LLMs and AMR Parsing
- United States Air Force（美国空军）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对不熟悉编程的用户需与ASP交互的问题，提出结合LLM和AMR解析将无约束英语转换为ASP程序的方法，LLM完成简单任务、AMR生成约束，成功解决逻辑谜题，为轻量可解释系统奠定基础。
AI中文摘要：
回答集编程（ASP）是基于逻辑编程和非单调推理的声明式编程范式，是描述和解决组合问题的强大工具。与其他语言一样，ASP要求用户学习其工作原理和语法。不熟悉编程语言的人越来越需要与代码交互。本文提出一种新方法，使用LLM和抽象意义表示（AMR）图将无约束英语转换为用于逻辑谜题的ASP程序，生成ASP规则、事实和约束以完整表示并解决目标问题。通过示例逻辑谜题展示系统能力：多数现有方法完全依赖LLM，而该系统仅让LLM完成简单任务（简化英语句子、识别关键词、生成简单事实），再从简化语言解析AMR图以系统生成ASP约束，成功创建解决组合逻辑问题的完整ASP程序。该方法是构建轻量、可解释的自然语言转逻辑问题求解系统的重要第一步。
英文摘要：
Answer Set Programming (ASP) is a declarative programming paradigm based on logic programming and non-monotonic reasoning. It is a tremendously powerful tool for describing and solving combinatorial problems. Like any other language, ASP requires users to learn how it works and the syntax involved. It is becoming increasingly required for those unfamiliar with programming languages to interact with code. This paper proposes a novel method of translating unconstrained English into ASP programs for logic puzzles using an LLM and Abstract Meaning Representation (AMR) graphs. Everything from ASP rules, facts, and constraints is generated to fully represent and solve the desired problem. Example logic puzzles are used to demonstrate the capabilities of the system. While most current methods rely entirely on an LLM, our system minimizes the role of the LLM only to complete straightforward tasks. The LLM is used to simplify natural language sentences, identify keywords, and generate simple facts. The AMR graphs are then parsed from simplified language and used to generate ASP constraints systematically. The system successfully creates an entire ASP program that solves a combinatorial logic problem. This approach is a significant first step in creating a lighter-weight, explainable system that converts natural language to solve complex logic problems.
