Related papers: X-CoT: Explainable Text-to-Video Retrieval via LLM-based Chain-of-Thought Reasoning

X-CoT: Explainable Text-to-Video Retrieval via LLM-based Chain-of-Thought Reasoning

URL: http://arxiv.org/abs/2509.21559v1
Date: Thu, 25 Sep 2025 20:39:45 GMT
Title: X-CoT: Explainable Text-to-Video Retrieval via LLM-based Chain-of-Thought Reasoning
Authors: Prasanna Reddy Pulakurthi, Jiamian Wang, Majid Rabbani, Sohail Dianat, Raghuveer Rao, Zhiqiang Tao,
Abstract summary: This work proposes X-CoT, an explainable retrieval framework upon LLM CoT reasoning.<n>We first expand the existing benchmarks with additional video annotations to support semantic understanding.<n>X-CoT empirically improves the retrieval performance and produces detailed rationales.
Score: 23.9465771255843
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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: https://github.com/PrasannaPulakurthi/X-CoT.

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