Generating Leakage-Free Benchmarks for Robust RAG Evaluation

📰 ArXiv cs.AI

Learn to generate leakage-free benchmarks for robust RAG evaluation to ensure reliable testing of retrieval-augmented generation models

advanced Published 12 May 2026

Action Steps

Identify potential knowledge leakage in existing RAG benchmark datasets
Use techniques such as data augmentation or dataset filtering to remove leakage
Generate new benchmark datasets that are leakage-free
Evaluate RAG models using the new leakage-free benchmarks
Compare results to existing benchmarks to assess the impact of knowledge leakage

Who Needs to Know This

NLP engineers and researchers working on RAG models can benefit from this knowledge to improve the evaluation of their models

Key Insight

💡 Knowledge leakage in RAG benchmark datasets can lead to unreliable evaluation results, highlighting the need for leakage-free benchmarks

Key Takeaways

Learn to generate leakage-free benchmarks for robust RAG evaluation to ensure reliable testing of retrieval-augmented generation models

Full Article

Title: Generating Leakage-Free Benchmarks for Robust RAG Evaluation

Abstract:
arXiv:2605.08838v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) is widely used to augment large language models (LLMs) with external knowledge. However, many benchmark datasets, designed to test RAG performance, comprise many questions that can already be answered from an LLM's parametric memory. This leads to unreliable evaluation. We refer to this phenomenon as knowledge leakage: cases where RAG tasks are solvable without retrieval. This issue worsens over time due to be

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