X-CoT: Explainable Text-to-Video Retrieval via LLM-based
  Chain-of-Thought Reasoning
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.
68da237c0177a6054b013be3
Prasanna Reddy Pulakurthi
claimed_verified
2025-09-29T11:26:22.375Z
68090a58090c3a12a2806615
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/0dMsD4hSMkQ1Vo5vkOiQd.png
PRASANNA REDDY PULAKURTHI
user
prasannareddyp
68da237c0177a6054b013be4
Jiamian Wang
68da237c0177a6054b013be5
Majid Rabbani
68da237c0177a6054b013be6
Sohail Dianat
68da237c0177a6054b013be7
Raghuveer Rao
68da237c0177a6054b013be8
Zhiqiang Tao
X-CoT: Explainable Text-to-Video Retrieval via LLM-based
  Chain-of-Thought Reasoning
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.
X-CoT, an explainable retrieval framework using LLM CoT reasoning, enhances text-to-video retrieval by providing detailed rationales and improving performance.
embedding models cosine similarities X-CoT LLM CoT reasoning video annotations semantic understanding data bias retrieval CoT pairwise comparison detailed rationales model behavior data quality analysis