Speaker: Val Andrei Fajardo
Summary
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The speaker discusses the evaluation of multimodal RAG systems using the LlamaIndex library. They explain the concept of retrieval augmented generation (rag) systems and how the LlamaIndex library serves as a data orchestration framework. The evaluation of RAG systems is split into retrieval and generation components, with metrics like hit rate and mean reciprocal rank for retrieval evaluation, and metrics like correctness, faithfulness, and relevancy for generation evaluation. The speaker demonstrates building a multimodal rag system for spelling in American Sign Language (ASL) and presents evaluation results. They also address questions about the LlamaIndex, measurement of correctness, faithfulness, and relevance, and introduce the Llama Hub portal. The speaker discusses challenges in evaluating language models and highlights the importance of open-source alternatives and multimodal research.
Topics
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⃝ Introduction to RAG Systems and LlamaIndex
RAG systems retrieve relevant context to generate answers
LlamaIndex is a python open-source library for building RAG systems
⃝ Evaluation of RAG Systems
Retrieval evaluation considers metrics like hit rate and mean reciprocal rank
Generation evaluation uses metrics like correctness, faithfulness, and relevancy
⃝ Building a Multimodal RAG System
Loading image and text documents
Indexing using multimodal vector store index
Creating the query engine
Measurement of correctness, faithfulness, and relevance
Introduction of Llama Hub portal
⃝ Challenges in Evaluating Language Models
Limitations of human evaluations
Importance of deterministic measures
Challenges of detecting and correcting hallucinations
Leveraging successful approaches from unimodal research
Негізгі бет Evaluation of Multimodal RAG Systems using the LlamaIndex
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