Measuring the coherence of text is a vital aspect of evaluating the quality of written content. Recent advancements in neural coherence modeling have demonstrated their efficacy in capturing entity coreference and discourse relations, thereby enhancing coherence evaluation. However, many existing methods heavily depend on static embeddings or focus narrowly on nearby context, constraining their capacity to measure the overarching coherence of long texts. In this paper, we posit that coherent texts inherently manifest a sequential and cohesive interplay among sentences, effectively conveying the central theme, purpose, or standpoint. To explore this abstract relationship, we introduce the "BBScore," a novel reference-free metric grounded in Brownian bridge theory for assessing text coherence. Our findings showcase that when synergized with a simple additional classification component, this metric attains a performance level comparable to state-of-the-art techniques on standard artificial discrimination tasks. We also establish in downstream tasks that this metric effectively differentiates between human-written documents and text generated by large language models under a specific domain. Furthermore, we illustrate the efficacy of this approach in detecting written styles attributed to diverse large language models, underscoring its potential for generalizability. In summary, we present a novel Brownian bridge coherence metric capable of measuring both local and global text coherence, while circumventing the need for end-to-end model training. This flexibility allows for its application in various downstream tasks.

翻译：文本连贯性测量是评估写作质量的重要方面。近年来，神经连贯性建模方法在捕捉实体共指关系和篇章关联方面展现出显著成效，从而提升了连贯性评估效果。然而，现有方法大多依赖静态嵌入或局限于邻近语境，限制了对长文本整体连贯性的测量能力。本文提出，连贯文本本质上体现为句子间具有顺序性和连贯性的相互作用，有效传达核心主题、目的或立场。为探索这种抽象关系，我们引入"BBScore"——一种基于布朗桥理论的新型无参考指标，用于评估文本连贯性。实验结果表明，该指标与简单分类组件协同使用时，在标准人工判别任务上能达到与最先进技术相媲美的性能。在下游任务中，我们证实该指标能有效区分特定领域内人类撰写的文档与大语言模型生成的文本。此外，我们展示了该方法在检测不同大语言模型写作风格方面的有效性，凸显其泛化潜力。总之，我们提出了一种新型布朗桥连贯性指标，既能测量局部文本连贯性也能测量全局文本连贯性，同时无需端到端模型训练，这种灵活性使其适用于多种下游任务。