Evaluation of Multimodal RAG Systems using the LlamaIndex
26Speaker: 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