Language models are often trained on text alone, without additional grounding. There is debate as to how much of natural language semantics can be inferred from such a procedure. We prove that entailment judgments between sentences can be extracted from an ideal language model that has perfectly learned its target distribution, assuming the training sentences are generated by Gricean agents, i.e., agents who follow fundamental principles of communication from the linguistic theory of pragmatics. We also show entailment judgments can be decoded from the predictions of a language model trained on such Gricean data. Our results reveal a pathway for understanding the semantic information encoded in unlabeled linguistic data and a potential framework for extracting semantics from language models.
翻译:语言模型往往只接受文字培训,而没有附加理由。关于从这种程序中可以推断出多少自然语言语义学的争论正在展开。我们证明,从一个完全了解其目标分布的理想语言模型中可以提取对判决的必然判断,假设培训判决是由Gricean代理商,即从实用语言理论中遵循基本沟通原则的代理人产生。我们还表明,从预测一种语言模型中可以解码出对此类Gricean数据进行培训的必然判断。我们的结果揭示了一种理解以未贴标签语言数据编码的语义信息的途径,以及从语言模型中提取语义学的潜在框架。