The paper describes a system that uses large language model (LLM) technology to support the automatic learning of new entries in an intelligent agent's semantic lexicon. The process is bootstrapped by an existing non-toy lexicon and a natural language generator that converts formal, ontologically-grounded representations of meaning into natural language sentences. The learning method involves a sequence of LLM requests and includes an automatic quality control step. To date, this learning method has been applied to learning multiword expressions whose meanings are equivalent to those of transitive verbs in the agent's lexicon. The experiment demonstrates the benefits of a hybrid learning architecture that integrates knowledge-based methods and resources with both traditional data analytics and LLMs.

翻译：本文描述了一种利用大语言模型（LLM）技术支持智能代理语义词典自动学习新条目的系统。该过程通过现有非玩具级词典和自然语言生成器进行初始化——该生成器能将基于本体的形式化意义表征转化为自然语言句子。学习方法涉及一系列LLM请求，并包含自动质量控制步骤。迄今，该方法已应用于学习与代理词典中及物动词意义等价的多词表达式。实验结果表明，将基于知识的方法与资源同传统数据分析及LLM相结合的混合学习架构具有显著优势。