标题： FaStFACT：在大语言模型中更快、更强大的长格式事实性评估 显示英文标题

标题： FaStFACT: Faster, Stronger Long-Form Factuality Evaluations in LLMs

摘要： 评估大型语言模型（LLMs）的长文本生成的真实性仍然具有挑战性，这是由于准确性问题和高昂的人工评估成本。以往的努力尝试通过将文本分解为声明，搜索证据并验证声明来实现这一点，但存在关键缺点：(1) 由于复杂的管道组件不适合长LLM输出，导致效率低下；(2) 由于声明集不准确和单行片段证据收集不足，导致效果不佳。为解决这些限制，我们提出了\name ，这是一个快速且强大的评估框架，在现有基线中实现了与人工评估最高的对齐度和效率。\name 首先采用基于置信度的逐块声明提取和预验证，显著降低了网络搜索和推理调用的成本，同时确保了可靠性。在搜索和验证方面，它从爬取的网页中收集文档级证据，并在验证期间选择性地检索，解决了之前管道中的证据不足问题。基于聚合和手动标注基准的大量实验证明了\name 在高效和有效地评估长文本LLM生成的真实性方面的可靠性。代码和基准数据可在 https://github.com/Yingjia-Wan/FastFact 获取。 显示英文摘要

摘要： Evaluating the factuality of long-form generations from Large Language Models (LLMs) remains challenging due to accuracy issues and costly human assessment. Prior efforts attempt this by decomposing text into claims, searching for evidence, and verifying claims, but suffer from critical drawbacks: (1) inefficiency due to complex pipeline components unsuitable for long LLM outputs, and (2) ineffectiveness stemming from inaccurate claim sets and insufficient evidence collection of one-line snippets. To address these limitations, we propose \name, a fast and strong evaluation framework that achieves the highest alignment with human evaluation and efficiency among existing baselines. \name first employs chunk-level claim extraction integrated with confidence-based pre-verification, significantly reducing the cost of web searching and inference calling while ensuring reliability. For searching and verification, it collects document-level evidence from crawled webpages and selectively retrieves it during verification, addressing the evidence insufficiency problem in previous pipelines. Extensive experiments based on an aggregated and manually annotated benchmark demonstrate the reliability of \name in both efficiently and effectively evaluating the factuality of long-form LLM generations. Code and benchmark data is available at https://github.com/Yingjia-Wan/FastFact.