用大模型自动生成语言策略，让推理更贴近真实对话。
Integrating Neural and Symbolic Components in a Model of Pragmatic Question-Answering
- 用大模型替代人工设定语言规则，自动生成语义和回答选项。
- 混合模型在预测人类回答模式上达到甚至超过传统模型表现。
- 适合研究语言认知、人机对话系统设计的学者与工程师。
计算语言学中的语用模型长期依赖人工设定的语句与语义集合，难以适应真实语言使用。本文提出一种神经符号框架，通过引入基于大语言模型（LLM）的模块，自动提出并评估自然语言中的关键成分，无需手动定义。以经典的语用问答案例为研究对象，系统考察了将神经模块融入认知模型的多种方法——从评估效用、解析字面语义，到生成替代语句和目标。结果表明，混合模型在预测人类回答模式方面可媲美或超越传统概率模型。但其成功高度依赖于大模型的整合方式：在生成替代方案和将抽象目标转化为效用方面表现优异，但在真值条件语义评估上仍存在挑战。该研究为构建更灵活、可扩展的语用语言模型指明方向，同时揭示了神经与符号组件平衡设计的关键考量。
原文摘要 · Abstract (English)
Computational models of pragmatic language use have traditionally relied on hand-specified sets of utterances and meanings, limiting their applicability to real-world language use. We propose a neuro-symbolic framework that enhances probabilistic cognitive models by integrating LLM-based modules to propose and evaluate key components in natural language, eliminating the need for manual specification. Through a classic case study of pragmatic question-answering, we systematically examine various approaches to incorporating neural modules into the cognitive model -- from evaluating utilities and literal semantics to generating alternative utterances and goals. We find that hybrid models can match or exceed the performance of traditional probabilistic models in predicting human answer patterns. However, the success of the neuro-symbolic model depends critically on how LLMs are integrated: while they are particularly effective for proposing alternatives and transforming abstract goals into utilities, they face challenges with truth-conditional semantic evaluation. This work charts a path toward more flexible and scalable models of pragmatic language use while illuminating crucial design considerations for balancing neural and symbolic components.
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