Beyond Relevance: On the Relationship Between Retrieval and RAG Information Coverage
Summary: arXiv:2603.08819v3 Announce Type: replace-cross
Abstract
Retrieval-augmented generation (RAG) systems combine document retrieval with a generative model to address complex information seeking tasks like report generation. While the relationship between retrieval quality and generation effectiveness seems intuitive, it has not been systematically studied. We investigate whether upstream retrieval metrics can serve as reliable early indicators of the final generated response’s information coverage.
Introduction
The rise of artificial intelligence has led to significant advancements in information retrieval and generation systems. Among these, retrieval-augmented generation (RAG) systems have emerged as a powerful tool to enhance the capabilities of traditional generative models. By integrating document retrieval processes, RAG systems aim to improve the quality and relevance of generated content. However, understanding the intricate relationship between retrieval and generation remains a crucial area for exploration.
Research Objectives
This study aims to systematically analyze the relationship between retrieval metrics and generation performance in RAG systems. Specifically, we focus on determining if retrieval metrics can predict the information coverage of generated responses. Our research is built upon experiments conducted across various benchmarks, allowing for a comprehensive assessment of retrieval effectiveness.
Methodology
We conducted experiments across two text RAG benchmarks (TREC NeuCLIR 2024 and TREC RAG 2024) and one multimodal benchmark (WikiVideo). The study involved analyzing 15 text retrieval stacks and 10 multimodal retrieval stacks across four RAG pipelines, utilizing multiple evaluation frameworks including Auto-ARGUE and MiRAGE.
Findings
Our findings reveal strong correlations between coverage-based retrieval metrics and nugget coverage in generated responses. This correlation is evident at both the topic and system levels. Key outcomes from our research include:
- Strong alignment between retrieval objectives and generation goals enhances information coverage.
- Complex iterative RAG pipelines may decouple generation quality from retrieval effectiveness.
- Retrieval metrics can serve as reliable proxies for assessing RAG performance.
Discussion
The implications of our findings suggest that stakeholders in AI development can leverage retrieval metrics to forecast the performance of RAG systems. By aligning retrieval processes with generative objectives, developers can enhance the overall effectiveness of information generation tasks. Additionally, this research highlights the potential for iterative improvements in RAG pipelines, emphasizing the need for further exploration in complex retrieval scenarios.
Conclusion
In conclusion, our study underscores the importance of understanding the interplay between retrieval and generation within RAG systems. The empirical support for using retrieval metrics as indicators of RAG performance opens new avenues for optimizing AI-driven information generation. As the field continues to evolve, ongoing research will be essential to deepen our understanding of these relationships and enhance the capabilities of retrieval-augmented systems.