How does PRISM framework's retrieval efficiency (latency and throughput) compare to end-to-end multi-hop QA pi

Abstract

Abstract: Large Language Models (LLMs) have demonstrated significant performance improvements across various cognitive tasks. An emerging application is using LLMs to enhance retrieval-augmented generation (RAG) capabilities. These systems require LLMs to understand user queries, retrieve relevant information, and synthesize coherent and accurate responses. Given the increasing real-world deployment of such systems, comprehensive evaluation becomes crucial. To this end, we propose FRAMES (Factuality, Retrieval, And reasoning MEasurement Set), a high-quality evaluation dataset designed to test LLMs' abil

Research Question

How does PRISM framework's retrieval efficiency (latency and throughput) compare to end-to-end multi-hop QA pipeline response time across different LLM architectures (7B, 13B, 30B parameter models) on MuSiQue and 2WikiMultihopQA benchmarks, as measured by F1 scores and inference time?

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Quality Dimensions

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Provenance