  We present a novel extension to Retrieval Augmented Generation with the goal
of mitigating factual inaccuracies in the output of large language models.
Specifically, our method draws on the cognitive linguistic theory of frame
semantics for the indexing and retrieval of factual information relevant to
helping large language models answer queries. We conduct experiments to
demonstrate the effectiveness of this method both in terms of retrieval
effectiveness and in terms of the relevance of the frames and frame relations
automatically generated. Our results show that this novel mechanism of Frame
Semantic-based retrieval, designed to improve Retrieval Augmented Generation
(FS-RAG), is effective and offers potential for providing data-driven insights
into frame semantics theory. We provide open access to our program code and
prompts.
