Enhancing RAG and Knowledge Graphs with Discourse

We consider a number of Retrieval Augmented Generation (RAG) architectures to address a lack of specific information and hallucination issues of Large Language Models (LLM)-based question answering.We start with conformal prediction which acts on top of LLM and maintains a set of generations instead of a single one and attempts to find the best element of this set, which is assumed to be the "most average one".We then proceed to LLM self-reflection series of RAG architectures predicting the multi-hop question answering session before actual search for an answer.After that, we propose a mechanism for LLM to filter out answers inappropriate with respect to style.All these components need discourse-level analysis for more robust functioning.Knowledge graph (KG) and Abstract Meaning Representation (AMR)-based knowledge graph construction follow.We evaluate the contribution of all of these components to overall answer relevance and also zoom in on the role of discourse-based subsystem in each of these components.There is a substantial improvement of performance due to the fourcomponent architecture introduced in this paper; the contribution of discourse-based subsystems is fairly modest.