Benchmarks and datasets for RAG evaluation
Standardized benchmarks and datasets play a crucial role in driving progress in RAG research and development. They provide a common ground for evaluating and comparing different RAG systems, facilitating the process of identifying best practices and tracking advancements over time.
Let’s look at some key benchmarks and datasets:
- Knowledge Intensive Language Tasks (KILT): A comprehensive benchmark for evaluating knowledge-intensive language tasks, including QA, fact-checking, dialogue, and entity linking:
- Data source: Based on Wikipedia, with a unified format for all tasks
- Strengths: Provides a diverse set of tasks, allows both retrieval and generation to be evaluated, and includes a standardized evaluation framework
- Limitations: Primarily based on Wikipedia, which might not reflect the diversity of real-world knowledge sources
- Natural Questions (NQ): A large-scale QA dataset collected from real user queries that is sent...