Papers by Shuoran Jiang
Multi-hop Graph Convolutional Network with High-order Chebyshev Approximation for Text Reasoning (2021.acl-long)
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| Challenge: | Existing single-hop graph reasoning in Graph convolutional networks may miss some important non-consecutive dependencies. |
| Approach: | They propose a graph convolutional network with the high-order dynamic Chebyshev approximation which augments multi-hop graph reasoning by fusing messages aggregated from direct and long-term dependencies into one convolutionalist layer. |
| Outcome: | The proposed model improves on four transductive and inductive NLP tasks and the ablation of the existing model. |
Linguistic Rule Induction Improves Adversarial and OOD Robustness in Large Language Models (2024.lrec-main)
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| Challenge: | Existing large language models (LLMs) do not perform satisfactorily in OOD and adversarial robustness evaluations. |
| Approach: | They propose to use linguistic rule induction to fine-tune large language models with linguistic rules to achieve better adversarial and OOD robustness. |
| Outcome: | The proposed model achieves comparable or better results with GPT-3.5 and GPT-4 on various adversarial and OOD robustness evaluations. |
Reasoning Graph Enhanced Exemplars Retrieval for In-Context Learning (2025.coling-main)
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| Challenge: | Existing methods focus on semantic similarity between queries and candidate exemplars, while logical connections between reasoning steps can be beneficial to depict problem-solving process. |
| Approach: | They propose a method to retrieve exemplars with semantic and structural similarity using a graph kernel. |
| Outcome: | The proposed method is superior to state-of-the-art retrieval-based approaches on mathematics and logical reasoning tasks. |