KEL-CoT:Knowledge-Enhanced Logical Chain-of-Thought for Political Perspective Detection
Authors: Xinming chen, Xu Liu, Shuai Zhong, Yuanxing Xu, Xiaotong Wang, Xuanning Liu, Bin Wu


Reviewer paper summaries: The paper proposes a knowledge-enhanced logical chain-of-thought (KEL-COT) method to avoid the introduction of redundant and fragmented knowledge in political polarity detection task. KEL-COT primarily comprises a knowledge retriever and a verifier, which retrieve necessary knowledge and verify logical consistency. The authors tested the proposed method on a single public dataset.
Summary of strengths: 1. The proposed KEL-COT method demonstrates superior performance in MAE.
2. The retrieval-and-verification paradigm presents a promising solution for political polarity detection.
Summary of weaknesses: 1. KEL-COT was evaluated on only one public dataset. It should be tested on more typical public datasets (e.g., P-Stance, Congress Speech) for comprehensive evaluation.
2. Some critical information is missing from the paper, such as the specific LLM used, the threshold value $K$, the ablation performance on different backbones and without the retriever module. 
3. The authors do not clearly state which LLM (e.g., Chat-3.5, Chat-4, Chat-4o, or an open-source LLM) was used in their research. The final results can vary significantly with different backbones.
Comments, suggestions and typos: 1. Please specify which LLM serves as the backbone for KEL-COT.
2. The distinction between $T_i$ and $B_i$ is unclear from their description (Page 3, Lines 191-193). 
3. Indicate the meanings of $N$, $T$, and $B$ in Figure 1 to improve the readability and clarity of the proposed method.
4. Correct typos of inverted commas and brackets in the manuscript.
Overall assessment scores: 1.5