Titlebook: Masters Theses in the Pure and Applied Sciences; Accepted by Colleges Wade H. Shafer Book 1986 Plenum Press, New York 1986 astronomy.calcul

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Wade H. Shaferl set. A majority of labels only have a few training instances due to large label dimensionality in XMTC. To solve this data sparsity issue, most existing XMTC methods take advantage of fixed label clusters obtained in early stage to balance performance on tail labels and head labels. However, such

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Wade H. Shaferteractive performance requirement. A typical solution is to retrieve the answer by finding the optimal query graph, which is a sub-graph of the knowledge graph. However, existing methods usually generate a considerable number of sub-graph candidates, then fail to find the optimal one effectively, re

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Wade H. Shaferpted to utilize techniques in Hierarchical Multi-label Text Classification (HMTC) to classify scientific literature. Although there have been many advances, some problems still cannot be effectively solved in HMTC tasks, such as the difficulty in capturing the dependencies of hierarchical labels and

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Wade H. Shaferiety of reasoning tasks. Still, due to the demand for low costs, research on the upper bound of small language models in reasoning tasks and the limitation of the knowledge they can accommodate has drawn attention. In line with previous work on math word problems, we discover that models that only l

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Wade H. Shaferng MWPs with two types of solvers: tree-based solver and large language model (LLM) solver. However, these approaches always solve MWPs by a single solver, which will bring the following problems: (1) Single type of solver is hard to solve all types of MWPs well. (2) A single solver will result in p