Retrieving Relevant Knowledge Subgraphs for Task-Oriented Dialogue

Publikasjonsdetaljer

  • Del av: Proceedings of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue (Association for Computational Linguistics, 2025)
  • Sider: 513–526
  • År: 2025
  • Lenke:

In this paper, we present an approach for extracting knowledge graph information for retrieval augmented generation in dialogue systems. Knowledge graphs are a rich source of background information, but the inclusion of more potentially useful information in a system prompt risks decreased model performance from excess context. We investigate a method of retrieving relevant subgraphs of maximum relevance and minimum size by framing this trade-off as a Prize-collecting Steiner Tree problem. The results of our user study and analysis indicate promising efficacy of a simple subgraph retrieval approach compared with a top-K retrieval model.