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VERA: Validation and Evaluation of Retrieval-Augmented Systems

Tianyu Ding, Adi Banerjee, Laurent Mombaerts, Yunhong Li, Tarik Borogovac, Juan Pablo De la Cruz Weinstein

TL;DR

VERA addresses the challenge of evaluating Retrieval-Augmented Generation systems by introducing a scalable, transparent framework that combines LLM-based integrity metrics with a cross-encoder consolidation to produce a single, actionable ranking score. It also introduces bootstrap statistics to quantify confidence bounds on metric distributions and assesses document repository topicality via contrastive analysis. The approach encompasses defined metrics (Faithfulness, Retrieval Recall, Retrieval Precision, Answer Relevance), a text-enhanced cross-encoder ranking mechanism, and robust topicality analysis, demonstrated across general and domain-specific datasets with multiple LLMs and retrievers. The results indicate that cross-encoder aggregation yields nuanced, reliable rankings and that bootstrap topicality analysis provides meaningful domain coverage signals, supporting more trustworthy deployment and iterative improvement of RAG systems. Overall, VERA contributes a practical, theory-backed methodology for reliable, interpretable evaluation of generative systems that rely on retrieved information, with clear implications for responsible AI deployment.

Abstract

The increasing use of Retrieval-Augmented Generation (RAG) systems in various applications necessitates stringent protocols to ensure RAG systems accuracy, safety, and alignment with user intentions. In this paper, we introduce VERA (Validation and Evaluation of Retrieval-Augmented Systems), a framework designed to enhance the transparency and reliability of outputs from large language models (LLMs) that utilize retrieved information. VERA improves the way we evaluate RAG systems in two important ways: (1) it introduces a cross-encoder based mechanism that encompasses a set of multidimensional metrics into a single comprehensive ranking score, addressing the challenge of prioritizing individual metrics, and (2) it employs Bootstrap statistics on LLM-based metrics across the document repository to establish confidence bounds, ensuring the repositorys topical coverage and improving the overall reliability of retrieval systems. Through several use cases, we demonstrate how VERA can strengthen decision-making processes and trust in AI applications. Our findings not only contribute to the theoretical understanding of LLM-based RAG evaluation metric but also promote the practical implementation of responsible AI systems, marking a significant advancement in the development of reliable and transparent generative AI technologies.

VERA: Validation and Evaluation of Retrieval-Augmented Systems

TL;DR

VERA addresses the challenge of evaluating Retrieval-Augmented Generation systems by introducing a scalable, transparent framework that combines LLM-based integrity metrics with a cross-encoder consolidation to produce a single, actionable ranking score. It also introduces bootstrap statistics to quantify confidence bounds on metric distributions and assesses document repository topicality via contrastive analysis. The approach encompasses defined metrics (Faithfulness, Retrieval Recall, Retrieval Precision, Answer Relevance), a text-enhanced cross-encoder ranking mechanism, and robust topicality analysis, demonstrated across general and domain-specific datasets with multiple LLMs and retrievers. The results indicate that cross-encoder aggregation yields nuanced, reliable rankings and that bootstrap topicality analysis provides meaningful domain coverage signals, supporting more trustworthy deployment and iterative improvement of RAG systems. Overall, VERA contributes a practical, theory-backed methodology for reliable, interpretable evaluation of generative systems that rely on retrieved information, with clear implications for responsible AI deployment.

Abstract

The increasing use of Retrieval-Augmented Generation (RAG) systems in various applications necessitates stringent protocols to ensure RAG systems accuracy, safety, and alignment with user intentions. In this paper, we introduce VERA (Validation and Evaluation of Retrieval-Augmented Systems), a framework designed to enhance the transparency and reliability of outputs from large language models (LLMs) that utilize retrieved information. VERA improves the way we evaluate RAG systems in two important ways: (1) it introduces a cross-encoder based mechanism that encompasses a set of multidimensional metrics into a single comprehensive ranking score, addressing the challenge of prioritizing individual metrics, and (2) it employs Bootstrap statistics on LLM-based metrics across the document repository to establish confidence bounds, ensuring the repositorys topical coverage and improving the overall reliability of retrieval systems. Through several use cases, we demonstrate how VERA can strengthen decision-making processes and trust in AI applications. Our findings not only contribute to the theoretical understanding of LLM-based RAG evaluation metric but also promote the practical implementation of responsible AI systems, marking a significant advancement in the development of reliable and transparent generative AI technologies.
Paper Structure (18 sections, 9 equations, 2 figures, 3 tables)

This paper contains 18 sections, 9 equations, 2 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. VERA method
  4. LLMs as Evaluators
  5. Consolidation of Multi-Dimensional Evaluation Metrics
  6. Bootstrap LLM-based RAG Evaluation Metrics
  7. Evaluating Document Repository Topicality Using Contrastive Query Analysis
  8. Experiments
  9. Models
  10. Datasets
  11. Results & Analysis
  12. VERA LLM-Based RAG Evaluation Metrics
  13. Bootstrap Metrics for Document Repository Topicality Analysis
  14. Conclusion
  15. Limitations
  16. ...and 3 more sections

Figures (2)

  • Figure 1: Overview of VERA: VERA begins with user queries, pairing them with retrieved and LLM summarized responses from a given RAG system. These elements form the basis for the LLM-based RAG evaluation of individual question-answer pairs, ensuring that the context relevance, answer faithfulness, and answer relevance metrics are meticulously assessed. These metrics are then consolidated using a cross-encoder to generate an aggregate score, enabling users to prioritize certain metrics over others and quickly make outcome-oriented decisions for development. The process then culminates with Bootstrap Statistics, which apply LLM-based metrics across the entire document repository to establish confidence bounds and gauge the overall performance of retrieval systems. This robust evaluation pipeline is essential for maintaining high standards of precision and trustworthiness in document retrieval, particularly critical in domains where the accuracy of information is paramount.
  • Figure 2: Example of Prompt of RAG Summarization with Retrieved Chunks