Response Quality Assessment for Retrieval-Augmented Generation via Conditional Conformal Factuality

• URL: http://arxiv.org/abs/2506.20978v1
• Date: Thu, 26 Jun 2025 03:52:56 GMT
• Title: Response Quality Assessment for Retrieval-Augmented Generation via Conditional Conformal Factuality
• Abstract summary: Conformal-RAG is a novel framework inspired by recent applications of conformal prediction (CP) on large language models (LLMs)<n>It offers statistical guarantees on the quality of refined sub-claims, ensuring response reliability without the need for ground truth answers.
• Score: 6.970520292379132
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Existing research on Retrieval-Augmented Generation (RAG) primarily focuses on improving overall question-answering accuracy, often overlooking the quality of sub-claims within generated responses. Recent methods that attempt to improve RAG trustworthiness, such as through auto-evaluation metrics, lack probabilistic guarantees or require ground truth answers. To address these limitations, we propose Conformal-RAG, a novel framework inspired by recent applications of conformal prediction (CP) on large language models (LLMs). Conformal-RAG leverages CP and internal information from the RAG mechanism to offer statistical guarantees on response quality. It ensures group-conditional coverage spanning multiple sub-domains without requiring manual labelling of conformal sets, making it suitable for complex RAG applications. Compared to existing RAG auto-evaluation methods, Conformal-RAG offers statistical guarantees on the quality of refined sub-claims, ensuring response reliability without the need for ground truth answers. Additionally, our experiments demonstrate that by leveraging information from the RAG system, Conformal-RAG retains up to 60\% more high-quality sub-claims from the response compared to direct applications of CP to LLMs, while maintaining the same reliability guarantee.

Related papers

• Is Conformal Factuality for RAG-based LLMs Robust? Novel Metrics and Systematic Insights [7.594584500009769]
  Large language models (LLMs) frequently hallucinate, limiting their reliability in knowledge-intensive applications.<n>Retrieval-augmented generation (RAG) and conformal factuality have emerged as potential ways to address this limitation.<n>We analyze the reliability and usefulness of conformal factuality for RAG-based LLMs across generation, scoring, calibration, robustness, and efficiency.
  arXiv  Detail & Related papers  (2026-03-17T17:20:08Z)
• Principled Context Engineering for RAG: Statistical Guarantees via Conformal Prediction [40.28465841863481]
  Retrieval-Augmented Generation (RAG) enhances factual grounding in large language models.<n>Existing pre-generation filters rely on confidence scores, offering no statistical control over retained evidence.<n>We demonstrate context engineering through conformal prediction, a coverage-controlled filtering framework.
  arXiv  Detail & Related papers  (2025-11-22T04:17:06Z)
• RAGalyst: Automated Human-Aligned Agentic Evaluation for Domain-Specific RAG [0.0]
  Retrieval-Augmented Generation (RAG) is a critical technique for grounding Large Language Models (LLMs) in factual evidence.<n>Existing evaluation frameworks often rely on metrics that fail to capture domain-specific nuances.<n>This paper introduces RAGalyst, an automated, human-aligned agentic framework designed for the rigorous evaluation of domain-specific RAG systems.
  arXiv  Detail & Related papers  (2025-11-06T16:22:52Z)
• FAIR-RAG: Faithful Adaptive Iterative Refinement for Retrieval-Augmented Generation [0.0]
  We introduce FAIR-RAG, a novel agentic framework that transforms the standard RAG pipeline into a dynamic, evidence-driven reasoning process.<n>We conduct experiments on challenging multi-hop QA benchmarks, including HotpotQA, 2WikiMultiHopQA, and MusiQue.<n>Our work demonstrates that a structured, evidence-driven refinement process with explicit gap analysis is crucial for unlocking reliable and accurate reasoning in advanced RAG systems.
  arXiv  Detail & Related papers  (2025-10-25T15:59:33Z)
• Improving Consistency in Retrieval-Augmented Systems with Group Similarity Rewards [14.535325886547112]
  RAG systems are increasingly deployed in high-stakes domains where users expect outputs to be consistent across semantically equivalent queries.<n>Existing systems often exhibit significant inconsistencies due to variability in both the retriever and generator.<n>We introduce a principled evaluation framework that decomposes RAG consistency into retriever-level, generator-level, and end-to-end components.
  arXiv  Detail & Related papers  (2025-10-05T23:14:13Z)
• RAG-Zeval: Towards Robust and Interpretable Evaluation on RAG Responses through End-to-End Rule-Guided Reasoning [64.46921169261852]
  RAG-Zeval is a novel end-to-end framework that formulates faithfulness and correctness evaluation as a rule-guided reasoning task.<n>Our approach trains evaluators with reinforcement learning, facilitating compact models to generate comprehensive and sound assessments.<n>Experiments demonstrate RAG-Zeval's superior performance, achieving the strongest correlation with human judgments.
  arXiv  Detail & Related papers  (2025-05-28T14:55:33Z)
• AlignRAG: Leveraging Critique Learning for Evidence-Sensitive Retrieval-Augmented Reasoning [61.28113271728859]
  RAG has become a widely adopted paradigm for enabling knowledge-grounded large language models (LLMs)<n>Standard RAG pipelines often fail to ensure that model reasoning remains consistent with the evidence retrieved, leading to factual inconsistencies or unsupported conclusions.<n>In this work, we reinterpret RAG as Retrieval-Augmented Reasoning and identify a central but underexplored problem: textitReasoning Misalignment.
  arXiv  Detail & Related papers  (2025-04-21T04:56:47Z)
• MES-RAG: Bringing Multi-modal, Entity-Storage, and Secure Enhancements to RAG [65.0423152595537]
  We propose MES-RAG, which enhances entity-specific query handling and provides accurate, secure, and consistent responses.<n>MES-RAG introduces proactive security measures that ensure system integrity by applying protections prior to data access.<n> Experimental results demonstrate that MES-RAG significantly improves both accuracy and recall, highlighting its effectiveness in advancing the security and utility of question-answering.
  arXiv  Detail & Related papers  (2025-03-17T08:09:42Z)
• MAIN-RAG: Multi-Agent Filtering Retrieval-Augmented Generation [34.66546005629471]
  Large Language Models (LLMs) are essential tools for various natural language processing tasks but often suffer from generating outdated or incorrect information.<n>Retrieval-Augmented Generation (RAG) addresses this issue by incorporating external, real-time information retrieval to ground LLM responses.<n>To tackle this problem, we propose Multi-Agent Filtering Retrieval-Augmented Generation (MAIN-RAG)<n>MAIN-RAG is a training-free RAG framework that leverages multiple LLM agents to collaboratively filter and score retrieved documents.
  arXiv  Detail & Related papers  (2024-12-31T08:07:26Z)
• Unanswerability Evaluation for Retrieval Augmented Generation [74.3022365715597]
  UAEval4RAG is a framework designed to evaluate whether RAG systems can handle unanswerable queries effectively.<n>We define a taxonomy with six unanswerable categories, and UAEval4RAG automatically synthesizes diverse and challenging queries.
  arXiv  Detail & Related papers  (2024-12-16T19:11:55Z)
• SFR-RAG: Towards Contextually Faithful LLMs [57.666165819196486]
  Retrieval Augmented Generation (RAG) is a paradigm that integrates external contextual information with large language models (LLMs) to enhance factual accuracy and relevance. We introduce SFR-RAG, a small LLM that is instruction-textual with an emphasis on context-grounded generation and hallucination. We also present ConBench, a new evaluation framework compiling multiple popular and diverse RAG benchmarks.
  arXiv  Detail & Related papers  (2024-09-16T01:08:18Z)
• RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework [66.93260816493553]
  This paper introduces RAGEval, a framework designed to assess RAG systems across diverse scenarios.<n>With a focus on factual accuracy, we propose three novel metrics: Completeness, Hallucination, and Irrelevance.<n> Experimental results show that RAGEval outperforms zero-shot and one-shot methods in terms of clarity, safety, conformity, and richness of generated samples.
  arXiv  Detail & Related papers  (2024-08-02T13:35:11Z)
• Evaluating RAG-Fusion with RAGElo: an Automated Elo-based Framework [0.5897092980823265]
  We propose a comprehensive framework to evaluate Retrieval-Augmented Generation (RAG) Question-Answering systems. We use Large Language Models (LLMs) to generate large datasets of synthetic queries based on real user queries and in-domain documents. We find that RAGElo positively aligns with the preferences of human annotators, though due caution is still required.
  arXiv  Detail & Related papers  (2024-06-20T23:20:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.