Abstract
Prevalent text-to-video retrieval systems mainly adopt embedding models for feature extraction and compute cosine similarities for ranking. However, this design presents two limitations. Low-quality text-video data pairs could compromise the retrieval, yet are hard to identify and examine. Cosine similarity alone provides no explanation for the ranking results, limiting the interpretability. We ask that can we interpret the ranking results, so as to assess the retrieval models and examine the text-video data? This work proposes X-CoT, an explainable retrieval framework upon LLM CoT reasoning in place of the embedding model-based similarity ranking. We first expand the existing benchmarks with additional video annotations to support semantic understanding and reduce data bias. We also devise a retrieval CoT consisting of pairwise comparison steps, yielding detailed reasoning and complete ranking. X-CoT empirically improves the retrieval performance and produces detailed rationales. It also facilitates the model behavior and data quality analysis. Code and data are available at: this https URL.
Abstract (translated)
当前的文本到视频检索系统主要采用嵌入模型进行特征提取，并通过计算余弦相似度来进行排序。然而，这种设计存在两个局限性：低质量的文本-视频数据对可能会损害检索效果，但这些低质量的数据却难以识别和审查；仅靠余弦相似度无法解释排名结果，从而限制了系统的可解释性。我们提出的问题是能否解释排名结果，以便评估检索模型并检查文本-视频数据？ 针对这些问题，这项工作提出了X-CoT（基于大规模语言模型CoT推理的可解释检索框架），用以替代基于嵌入模型的相似度排序方法。首先，我们在现有的基准测试基础上增加了额外的视频注释，支持语义理解和减少数据偏差。此外，我们设计了一种检索CoT（通过成对比较步骤生成详细的推理和完整的排名）。X-CoT在实践中提高了检索性能，并产生详细的原因说明。它还促进了模型行为和数据质量分析。 代码和数据可以在以下链接获取：[此URL](this https URL)
URL
https://arxiv.org/abs/2509.21559
