SPIRIT: Structural Entropy Guided Prefix Tuning for Hierarchical Text Classification
Authors: He Zhu, Jinxiang Xia, Yue Hou, Ruomei Liu, Jiaheng Liu, Shangzhe Li, Junran Wu, Ke Xu


Reviewer paper summaries: This paper addresses the challenges in Hierarchical Text Classification (HTC), a task that requires classifiers to understand both the semantic content of texts and the hierarchical dependencies within the label set. Existing prompt fine-tuning methods have shown limited performance when dealing with complex label hierarchies. Therefore, the authors propose a structured entropy-guided prefix fine-tuning method, SPIRIT, to improve the performance of hierarchical text classification.
Summary of strengths: 1. The paper proposes a very novel fine-tuning technique to address the challenges in hierarchical text classification (HTC).
2. This is the first work on prefix fine-tuning in HTC.
Summary of weaknesses: 1. Some parts of the narrative lack logical clarity. For instance, lines 051-053 state, "exhibits a potential risk wherein the attention scores on the prompt contents display continual downward trends as the input data flows toward the output layer." The authors do not explain why the decline in attention scores would lead to a potential risk.
2. The logic in lines 060-061 is also somewhat lacking: "Considering the two observations, prefix tuning-based HTC provides a promising direction for better hierarchical semantic extraction." The previous observations only highlight the limitations of vanilla prompt tuning and do not justify why prefix tuning-based HTC offers a promising direction.
3. From Table 2, it can be seen that the algorithm presented in this paper provides very limited improvement, which restricts the applicability and contribution of the method.
Comments, suggestions and typos: None
Overall assessment scores: 3