基于RAG的LLM的符合事实性是否具有鲁棒性？新颖的度量和系统性洞察
Is Conformal Factuality for RAG-based LLMs Robust? Novel Metrics and Systematic Insights
AI总结：
本文系统分析了RAG-based LLMs在符合事实性方面的可靠性与实用性，提出新的信息度量方法，发现高事实性水平导致输出空洞，符合事实性不鲁棒于分布偏移，轻量蕴含验证器更高效且表现优异。
摘要：
大型语言模型（LLMs）经常产生幻觉，限制了其在知识密集型应用中的可靠性。检索增强生成（RAG）和符合事实性已作为解决这一限制的潜在方法。虽然RAG旨在通过检索证据来支撑响应，但无法提供最终输出正确的统计保证。符合事实性过滤通过在保留数据上校准的阈值对原子声明进行评分和过滤，提供分布无关的统计可靠性，但最终输出的信息性无法保证。我们系统地分析了RAG-based LLMs在生成、评分、校准、鲁棒性和效率方面的可靠性与实用性。我们提出了新的信息度量方法，以更好地反映在符合过滤下的任务实用性。在三个基准和多个模型家族上，我们发现（i）符合过滤在高事实性水平下因输出空洞导致低实用性，（ii）符合事实性保证不鲁棒于分布偏移和干扰项，突显了需要校准数据与部署条件密切匹配的限制，（iii）轻量蕴含验证器在效率上优于基于LLM的模型置信度评分器，且需要少于100倍的FLOPs。总体而言，我们的结果揭示了事实性与信息性之间的权衡以及符合过滤框架在分布偏移和干扰项下的脆弱性，突显了需要新的方法来实现可靠性和鲁棒性与实用性的关键指标，并为构建既可靠又计算高效的RAG管道提供了可行指导。
英文摘要：
Large language models (LLMs) frequently hallucinate, limiting their reliability in knowledge-intensive applications. Retrieval-augmented generation (RAG) and conformal factuality have emerged as potential ways to address this limitation. While RAG aims to ground responses in retrieved evidence, it provides no statistical guarantee that the final output is correct. Conformal factuality filtering offers distribution-free statistical reliability by scoring and filtering atomic claims using a threshold calibrated on held-out data, however, the informativeness of the final output is not guaranteed. We systematically analyze the reliability and usefulness of conformal factuality for RAG-based LLMs across generation, scoring, calibration, robustness, and efficiency. We propose novel informativeness-aware metrics that better reflect task utility under conformal filtering. Across three benchmarks and multiple model families, we find that (i) conformal filtering suffers from low usefulness at high factuality levels due to vacuous outputs, (ii) conformal factuality guarantee is not robust to distribution shifts and distractors, highlighting the limitation that requires calibration data to closely match deployment conditions, and (iii) lightweight entailment-based verifiers match or outperform LLM-based model confidence scorers while requiring over $100\times$ fewer FLOPs. Overall, our results expose factuality-informativeness trade-offs and fragility of conformal filtering framework under distribution shifts and distractors, highlighting the need for new approaches for reliability with robustness and usefulness as key metrics, and provide actionable guidance for building RAG pipelines that are both reliable and computationally efficient.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
