Enhancing Stance Detection via Tree-Structured Knowledge Retrieval
Authors: Fuqiang Niu, Xianghua Fu, Genan Dai, Bowen Zhang


Reviewer paper summaries: This paper proposes a tree-structured retrieval-augmented (TSRA) framework, which enhances background knowledge by employing a Background Tree (BKTree) and Tree-of-Thoughts (ToT) to improve stance detection performance. The BKTree stores fragmented text segments, while ToT selects enhanced knowledge from the tree root to nodes.
Summary of strengths: 1. The paper provides a tree-structured retrieval-augmented method to incorporate background knowledge into the stance detection task.
2. The paper's writing is easy to read.
Summary of weaknesses: 1.  The two essential modules of the methodology lack strict guarantees to support their arguments. 
     The similarity-based knowledge tree building (KBTree) and the optimal knowledge retrieval (ToT) are all heavily reliant on the performance of prompt-based LLMs.
2. The authors claim to address the issue of fragmented text segments in stance detection, but do not provide a specific example to show how these fragments are integrated into a complete context.
3. The background knowledge is target-based infusion from wikipedia, VAST is more presentative dataset with diverse target than P-stance (3 targets) and COVID19(4 targets), only zero-shot performance reported on VAST. Moreover, important knowledge enhancement baselines are missing.
4. The methodology is an incremental improvement based on existing works in knowledge enhanced stance detection task.
Comments, suggestions and typos: 1. How is the background knowledge distinguished between sources like Wikipedia and backbone models (e.g., BERT, ChatGPT)?
2. Please provide some illustrations of the selected background knowledge by ToT. 
3. A comparison between fragmented text segments and the integrated context is necessary.
4. Few-short/supervised setting performance report on diverse-target VAST is needed, as authors claim their method benefits from background knowledge.
Overall assessment scores: 1.5