MIRAGE-Bench: An Automatic Multilingual Benchmark for Retrieval-Augmented Generation Systems

Understanding Retrieval-Augmented Generation (RAG) Retrieval-Augmented Generation (RAG) is a method that helps Large Language Models (LLMs) provide better answers to complex questions. It does this by finding relevant information before creating a response, which improves accuracy and reduces misinformation. RAG allows LLMs to cite sources, making it easier to verify facts. Example of RAG in Action A good example of RAG is Microsoft’s Bing Search. It combines information retrieval and citation techniques to give reliable answers. However, many RAG models mainly focus on English, which limits their use in other languages. Evaluating RAG Systems There are two main ways to evaluate RAG systems: 1. **Heuristic-based benchmarks**: These use various measures but rely on human judgment, making it hard to rank models clearly. 2. **Arena-based benchmarks**: These compare outputs directly but can be costly and resource-intensive. Introducing MIRAGE-BENCH A team from the University of Waterloo and VECTARA created MIRAGE-BENCH to improve how we evaluate RAG systems. This new framework is cost-effective and assesses multilingual generation in 18 languages. It uses a dataset called MIRACL, which includes relevant Wikipedia sections and human-curated questions. Key Features of MIRAGE-BENCH - It evaluates responses based on seven important factors, including fluency and citation quality. - It uses a Machine Learning model to score responses efficiently without needing expensive LLMs each time. - It adapts to new evaluation standards and correlates well with costly models like GPT-4o. Benefits of MIRAGE-BENCH MIRAGE-BENCH is beneficial for smaller LLMs and improves the efficiency of multilingual RAG evaluations. This allows for more thorough assessments across different languages. Contributions of the Research Team - Developed MIRAGE-BENCH to advance multilingual RAG research. - Created a model that balances efficiency and accuracy in evaluations. - Analyzed the strengths and weaknesses of 19 multilingual LLMs. Get Involved For more insights, check out the research paper and follow us on social media. If you appreciate our work, subscribe to our newsletter. Transform Your Business with AI Stay competitive by using MIRAGE-BENCH and other AI solutions: - **Identify Automation Opportunities**: Find areas in customer interactions that can benefit from AI. - **Define KPIs**: Ensure measurable impacts from your AI projects. - **Select an AI Solution**: Choose tools that fit your needs and can be customized. - **Implement Gradually**: Start with a pilot project, gather data, and expand wisely. For AI KPI management advice, contact us. For ongoing insights, follow us on social media. Explore AI Solutions Discover how AI can improve your sales processes and customer engagement.