PLaTo: Parameterized Label Topology for Multi-label Classification
Authors: Max Eichler, Xiaodan Zhu, Iryna Gurevych


Reviewer paper summaries: This paper addresses the application of label topology in large-scale multi-label classification, particularly its integration with large language models (LLMs). Traditional classification methods insufficiently utilize label topology, limiting their potential to enhance task performance. The paper proposes a novel method aimed at improving label representation and enhancing the model's understanding of subtle differences between labels through the generation of contrastive label descriptions.
Summary of strengths: - The idea of using the intermediate layers of language models to train local hierarchical classifiers is very innovative.
- The research problem is highly valuable, specifically how to efficiently utilize label topology when training LLMs for large-scale multi-label classification.
- The authors provide an in-depth analysis of their findings (§5.4).
- This is the first work to combine large-scale hierarchical multi-label classification with the utilization of label topology in LLMs.
- The experimental section is very detailed in explaining the implementation, enhancing reproducibility.
Summary of weaknesses: - The term "Large-scale" needs clarification, as the datasets used by the authors do not seem to be truly large-scale (compared to the datasets with millions of labels or instances).
- The choice of CascadeXML for experiments is questionable, as it is a method from the XML domain and may not be suitable for scenarios with a limited label space. Are there more appropriate methods?
- In Table 2, why does Llama 3 perform better than Llama 3 + PLATO on the MIMIC dataset in the full setup? Does this mean that Llama 3 + PLATO method didn't perform well on this dataset?
Comments, suggestions and typos: There is a slight mismatch between the title and the body of the paper. The title focuses on multi-label classification, whereas the body emphasizes large-scale and hierarchical multi-label classification.
Overall assessment scores: 3.5