Know Your RAG: Dataset Taxonomy and Generation Strategies for Evaluating RAG Systems (2411.19710v1)

Abstract: Retrieval Augmented Generation (RAG) systems are a widespread application of LLMs in the industry. While many tools exist empowering developers to build their own systems, measuring their performance locally, with datasets reflective of the system's use cases, is a technological challenge. Solutions to this problem range from non-specific and cheap (most public datasets) to specific and costly (generating data from local documents). In this paper, we show that using public question and answer (Q&A) datasets to assess retrieval performance can lead to non-optimal systems design, and that common tools for RAG dataset generation can lead to unbalanced data. We propose solutions to these issues based on the characterization of RAG datasets through labels and through label-targeted data generation. Finally, we show that fine-tuned small LLMs can efficiently generate Q&A datasets. We believe that these observations are invaluable to the know-your-data step of RAG systems development.

• The paper introduces a dataset taxonomy that classifies question-context pairs as fact, summary, reasoning, or unanswerable to refine RAG system evaluation.
• It demonstrates that imbalanced label distributions in public datasets distort retrieval performance, highlighting the need for tailored evaluation strategies.
• By fine-tuning small LLMs with LoRA, the authors generate cost-effective, balanced Q{content}A datasets that enable comprehensive RAG assessments.

Insights into RAG Dataset Taxonomy and Generation Strategies

This paper presents an in-depth analysis and novel methodologies regarding the evaluation of Retrieval Augmented Generation (RAG) systems, a prominent application of LLMs. The authors identify critical issues concerning the utilization of public datasets for RAG evaluation, indicating that such datasets can lead to an unbalanced representation of real-world query interactions, subsequently resulting in suboptimal system designs. Furthermore, they propose a taxonomy for question-context pairs and offer strategies to generate Q{content}A datasets to mitigate these challenges, emphasizing the importance of understanding the dataset composition in RAG systems development.

Key Highlights:

• Dataset Taxonomy: The authors introduce labels—fact, summary, reasoning, and unanswerable—to classify (context, query) pairs based on the nature of the answer provided by the context. This taxonomy is crucial for accurately measuring RAG system performance since it aligns with the varying degrees of difficulty associated with retrieving appropriate information. The paper indicates a significant imbalance in these labels across several popular RAG evaluation datasets, which can mislead performance evaluation and tuning of RAG systems.
• Impact on Retrieval Performance: The paper demonstrates that the performance of retrieval strategies is heavily influenced by the dataset's label composition. Performance evaluations using inclusive datasets may not reflect label-specific performance, leading to different optimal configurations for retrieval strategies. This finding stresses the necessity of adapting evaluation processes to reflect anticipated real-world question types to ensure robust RAG system performance.
• Synthetic Dataset Generation: Traditional methods of generating Q{content}A datasets, including those using single LLM prompts, tend to result in datasets dominated by fact questions, potentially limiting the comprehensive evaluation of RAG systems. The authors propose a multi-step strategy involving factual statement extraction, followed by question generation, which results in a more balanced representation of question types. Notably, by fine-tuning small LLMs using LoRA, the authors provide a cost-effective alternative for generating Q{content}A datasets. Despite the high cost and complexity often associated with LLM-based data generation, the fine-tuned model enables scalable and efficient production of varied question types, facilitating improved system evaluation.

Implications and Future Directions:

The implications of this research are significant for both theoretical and practical applications of RAG systems. On a theoretical level, the proposed taxonomy enriches the understanding of dataset composition's role in RAG performance. Practically, the ability to generate balanced datasets using fine-tuned, compact models offers a more accessible path for developers to test and refine their systems. This work paves the way for further research on refining dataset generation techniques and enhancing the evaluation infrastructure for RAG systems. As LLMs continue to evolve, so will the methodologies for effectively leveraging them in RAG applications, emphasizing the dynamic interplay between LLM capabilities and dataset characteristics.

In conclusion, this paper provides a crucial step towards refining RAG systems evaluation through a deeper understanding of dataset taxonomy and generation strategies. The findings challenge researchers and practitioners to consider the implications of dataset composition actively, advocating for data generation techniques that are both cost-effective and reflective of real-user interactions. This work not only advances current methodologies but also sets a foundation for the continued enhancement of RAG evaluations amidst the evolving landscape of AI technologies.