Neurosymbolic AI in Search with Professor Laura Dietz - Weaviate Podcast #49!

May 25, 2023

Professor Laura Dietz discusses Neurosymbolic Search, Entity Linking, Entity Re-Ranking, Knowledge Graphs, and Large Language Models. They explore the potentials of bias in using LLMs for relevance judgments and the complexities of merging neural technologies with symbolic systems in search technology. The conversation delves into enhancing search algorithms, filtered vector search, entity linking with context-specific models, and the nuances of relevance judgments in research papers.

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Introduction

00:00 • 2min

Exploring the Significance of Symbols in Information Retrieval

01:45 • 18min

20:13 • 2min

Enhancing Search Algorithms through Entity Ranking and Knowledge Graphs

22:14 • 4min

Exploring South America and the Zika Outbreak

26:28 • 2min

Enhancing Search Results with Text Retrieval Methods

28:05 • 12min

Exploring Filtered Vector Search and Adding Symbolic Data in Search Engines

39:43 • 2min

Enhancing Entity Linking with Context-specific Models

41:23 • 6min

Exploring the Complexities of Using Large Language Models for Relevance Judgments

47:27 • 17min

Navigating the Complexity of Relevance Judgments in Research Papers

01:04:14 • 17min

Evolution of Technology and Machine Intelligence

01:21:42 • 9min

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=ao ECIR 23 Tutorial: Neuro-Symbolic Approaches for Information Retrieval: https://www.cs.unh.edu/~dietz/appendix/dietz2023neurosymbolic.pdf Please 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.pdf Chapters 0:00 Introduction 0:15 Neurosymbolic Search 4:50 Entity Parsing and Vector Semantics 10:56 Query Intent Understanding 15:35 Knowledge Graphs and Vector Search 17:37 Symbolic Re-Ranking 22:10 ColBERT and Entity Ranking 26:25 Example - South America and Zika Virus IR 29:15 Knowledge Graph Query Languages with LLMs 35:10 We need more Knowledge Graphs!! 37:30 PrimeKG from Harvard BMI 39:40 Filtered Vector Search 42:20 LLM Entity Linking - “The” example 47:30 Cross Encoder Entity Focus? 48:25 Perspectives on LLMs for Relevance Judgments 55:28 Spectrum of Human-Machine Collaboration for Labeling 57:30 Use LLM to Create Relevance Labeling Interfaces 1:02:30 Importance for Weaviate 1:03:45 12 Authors’ 3 Conclusions 1:04:40 IR Research Community Challenge 1:06:55 Query Generation for Weaviate Users 1:13:05 Clustering Queries 1:17:30 Final Thoughts