用形式语义学解析自然语言推理，实现100%准确的逻辑排序推断。
FSLI: An Interpretable Formal Semantic System for One-Dimensional Ordering Inference
- 将自然语言转化为一阶逻辑，通过约束逻辑编程求解
- 在BIG-bench任务上达100%准确率，优于大模型基线
- 适合需要可解释推理的逻辑任务研究者
我们开发了一个系统，通过将自然语言前提和候选陈述转换为一阶逻辑，解决一维排序逻辑推理问题。基于Heim和Kratzer的基于语法的组合语义规则（利用λ演算），我们设计了一种带有抽象类型、模板规则和动态上下文实体解释机制的语义解析算法。生成的逻辑形式通过约束逻辑编程执行，以判断哪些候选陈述可从前提中逻辑推导出。该符号系统——形式语义逻辑推断器（FSLI）——提供了一个形式化基础且语言驱动的自然语言逻辑推理框架。我们在合成及衍生的逻辑推理任务上进行了评估：FSLI在BIG-bench的逻辑推理任务上达到100%准确率，在AR-LSAT的语法简化子集上达到88%，优于LLM基线o1-preview。尽管当前自然语言推理研究多聚焦于神经语言模型，FSLI凸显了原理性、可解释系统在NLP符号逻辑推断中的潜力。
原文摘要 · Abstract (English)
We develop a system for solving logical deduction one-dimensional ordering problems by transforming natural language premises and candidate statements into first-order logic. Building on Heim and Kratzer's syntax-based compositional semantic rules which utilizes lambda calculus, we develop a semantic parsing algorithm with abstract types, templated rules, and a dynamic component for interpreting entities within a context constructed from the input. The resulting logical forms are executed via constraint logic programming to determine which candidate statements can be logically deduced from the premises. The symbolic system, the Formal Semantic Logic Inferer (FSLI), provides a formally grounded, linguistically driven system for natural language logical deduction. We evaluate it on both synthetic and derived logical deduction problems. FSLI achieves 100% accuracy on BIG-bench's logical deduction task and 88% on a syntactically simplified subset of AR-LSAT outperforming an LLM baseline, o1-preview. While current research in natural language reasoning emphasizes neural language models, FSLI highlights the potential of principled, interpretable systems for symbolic logical deduction in NLP.
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