Papers by Oscar Chew
Understanding and Mitigating Spurious Correlations in Text Classification with Neighborhood Analysis (2024.findings-eacl)
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| Challenge: | Recent research has revealed that machine learning models have a tendency to leverage spurious correlations that exist in the training set but may not hold true in general circumstances. |
| Approach: | They propose a metric to detect spurious tokens and a family of regularization methods to mitigate spurious correlations in text classification. |
| Outcome: | The proposed method prevents spurious clusters and significantly improves the robustness of classifiers without auxiliary data. |
The Role of Exploration Modules in Small Language Models for Knowledge Graph Question Answering (2025.acl-srw)
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| Challenge: | Existing methods to integrate knowledge graphs into large language models often rely on proprietary or extremely large models . |
| Approach: | They propose to integrate knowledge graphs into reasoning processes of large language models . they propose to use simple and efficient exploration modules to handle knowledge graph traversal . |
| Outcome: | The proposed modules improve the performance of small language models on knowledge graph question answering tasks. |