Papers by Hanfang Yang
ALTER: Augmentation for Large-Table-Based Reasoning (2025.naacl-long)
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| Challenge: | Recent studies have focused on the use of large language models (LLMs) for table-based reasoning, but most approaches struggle with scalability when applied to large tables. |
| Approach: | They propose a framework to harness latent augmentation potential in tabular data . they use only a small subset of relevant data from the table to supplement it with schema . |
| Outcome: | The proposed framework outperforms all other approaches and exhibits robustness and efficiency against perturbations in large-table scenarios. |
MTRec: Multi-Task Learning over BERT for News Recommendation (2022.findings-acl)
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| Challenge: | Existing news recommendation methods learn news representations solely based on news titles. Existing methods only utilize title information and neglect other valuable news information such as categories and entities. |
| Approach: | They propose a multi-task method to incorporate multi-field information into BERT, which improves its news encoding capability. |
| Outcome: | Extensive experiments on the MIND news recommendation benchmark show the proposed method is effective. |