Active Retrieval Augmented Generation (FLARE) Explained!

This video explains the details behind Active Retrieval Augmented Generation from Jiang et al! This is a super exciting innovation on how exactly we construct retrieval-augmented generation pipelines that hand off from Weaviate to Large Language Models! Please see the links below for more information about particular parts of the paper analysis! Please leave a like and subscribe if you want to see more content like this, thank you so much for watching! Links: Active Retrieval Augmented Generation - https://arxiv.org/pdf/2305.06983.pdf OpenAI Playground - https://platform.openai.com/playground 2WikiMultiHopQA - https://aclanthology.org/2020.coling-main.580.pdf ASQA - https://aclanthology.org/2022.emnlp-main.566.pdf StrategyQA - https://arxiv.org/pdf/2101.02235.pdf WikiAsp - (sorry the url link was too long, this is from Hayashi et al.) UniEval - https://aclanthology.org/2022.emnlp-main.131.pdf Chapters 0:00 Welcome! 0:25 FLARE in 2 Minutes! 3:12 Retrieval-Augmented Generation 4:44 The Intuition 7:17 Algorithm Details 8:23 When to Query 9:50 Log Prob from OpenAI!!!! 11:58 How to Query 14:51 Baseline Approaches 17:00 Self-Ask Prompting 18:18 FLARE[indirect] (Search Action Ablation) 25:35 Evaluation Datasets 28:05 2WikiMultihopQA 29:55 StrategyQA 31:22 ASQA 32:10 WikiAsp 32:48 Summary of Evaluation Datasets 34:14 Evaluation Metrics 36:45 UniEval 37:54 Results! 41:45 Personal Reflections and Ideas