The FRAMES dataset is designed to provide a comprehensive evaluation of retrieval-augmented generation (RAG) systems. Unlike existing benchmarks that focus on isolated aspects, FRAMES tests the factuality, retrieval, and reasoning capabilities of RAG systems simultaneously.
The dataset consists of 824 challenging multi-hop questions that require integrating information from multiple Wikipedia articles. The questions cover a diverse range of topics and reasoning types, including numerical reasoning, tabular reasoning, multiple constraints, temporal reasoning, and post-processing.
The authors conducted experiments with state-of-the-art language models, including Gemini-Pro-1.5-0514, Gemini-Flash-1.5-0514, Gemma2-27b, and Gemma2-9b. The results show that even advanced models struggle significantly with the complex scenarios presented in FRAMES, achieving only moderate improvements when multi-step retrieval and reasoning strategies were employed.
The authors propose a multi-step retrieval and reasoning framework that compels models to iteratively retrieve and reason, significantly enhancing their performance on complex queries. The findings underscore the importance of further enhancing both the retrieval mechanisms and the reasoning capabilities of these models to improve their overall performance in real-world applications.
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by Satyapriya K... kl. arxiv.org 09-20-2024
https://arxiv.org/pdf/2409.12941.pdfDybere Forespørgsler