1. Understanding of Summary Indexing and the Prompts (as well as Prompt Chains) used to build them.

2. Continued development of LLM-generated data for search -- creating (full text, summary) pairs gives you (1) data to build a summary index with as mentioned, (2) data to compare different embedding models with, and (3) data to train your own embedding model.

3. Tournament style evaluation with human annotators -- the top 5 retrieved texts from one model are concatenated with the top 5 from another model, these 10 are given to human annotators to pick 5 and this is how the authors are reporting the performance of their models rather than traditional benchmarks. This m ay be a more productive evaluation technique for most real world search applications.

Thank you so much for watching, here are some links mentioned in the video!

Retrieving Texts based on Abstract Descriptions: https://arxiv.org/abs/2305.12517

Weaviate Blog - Combining LangChain and Weaviate: https://weaviate.io/blog/combining-langchain-and-weaviate

Weaviate Blog - Generative Feedback Loops: https://weaviate.io/blog/generative-feedback-loops-with-llms

Jerry Liu in Llama Index Blog - A New Document Summary Index for LLM-powered QA Systems: https://medium.com/llamaindex-blog/a-new-document-summary-index-for-llm-powered-qa-systems-9a32ece2f9ec

Learning to Retrieve Passages without Supervision (Spider): https://arxiv.org/pdf/2112.07708.pdf

Weaviate Blog - Analysis of Spider - https://weaviate.io/blog/research-insights-spider

Chapters

0:00 Introduction

0:13 Quick Overview

7:30 How to use in Weaviate!

7:50 Background

12:08 Motivation

14:20 Prompts Used

18:14 More Details of training

21:12 Human Evaluation Study

22:40 My Takeaways from the Paper