Hey everyone, thank you so much for watching the 49th episode of the Weaviate Podcast!! This podcast features Professor Laura Dietz from the University of New Hampshire! I came across Dr. Dietz's tutorial at ECIR on Neuro-Symbolic Approaches for Information Retrieval and am so grateful that she was interested in joining the Weaviate Podcast! I learned so much about Neurosymbolic Search, especially around the role of Entity Linking and Entity Re-Ranking -- as well as the topic of Knowledge Graphs and Vector Search. We also discussed Prof. Dietz and collaborators latest perspectives paper on Large Language Models for Relevance Judgment. TLDR this describes the idea of using LLMs to either generate synthetic queries for documents or say annotate the relevance for query, document pairs. We discussed this kind of idea with Leo Boytsov and his work on InPars, and have presented Promptagator on past episodes of the Weaviate Air show. Although this idea comes with a lot of potential, Dr. Dietz explains the potentials for bias and poor judgements, as well as generally diving more into the details of this kind of idea! I really hope you enjoy the podcast, we are more than happy to answer any questions you might have about these ideas, or discuss any of your ideas! Thanks so much for watching!Check out Laura Dietz's Publications here: https://scholar.google.com/citations?user=IIXpJ8oAAAAJ&hl=en&oi=aoECIR 23 Tutorial: Neuro-Symbolic Approachesfor Information Retrieval: https://www.cs.unh.edu/~dietz/appendix/dietz2023neurosymbolic.pdfPlease check this paper out below, I think this is a severely underrated work in the Search / Information Retrieval community:Perspectives on Large Language Models for Relevance Judgment: https://arxiv.org/pdf/2304.09161.pdfChapters