Papers by Yajing Sun
Bi-directional CognitiveThinking Network for Machine Reading Comprehension (2020.coling-main)
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| Challenge: | Existing methods for reading comprehension are still in their infancy at the level of cognitive intelligence. |
| Approach: | They propose a bi-directional cognitive knowledge framework to simulate reverse thinking and inertial thinking in the brain to answer questions. |
| Outcome: | The proposed framework shows that bi-directional knowledge helps the QA task. |
Have We Designed Generalizable Structural Knowledge Promptings? Systematic Evaluation and Rethinking (2025.acl-long)
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Yichi Zhang, Zhuo Chen, Lingbing Guo, Yajing Xu, Shaokai Chen, Mengshu Sun, Binbin Hu, Zhiqiang Zhang, Lei Liang, Wen Zhang, Huajun Chen
| Challenge: | Existing methods to integrate external knowledge into LLMs focus on specific problems, lacking a comprehensive exploration of the generalization and capability boundaries of SKP. |
| Approach: | They propose a new paradigm for structural knowledge prompting to integrate external structural knowledge into LLMs by incorporating structural representations. |
| Outcome: | The proposed benchmark SUBARU enables the evaluation of the generalization capabilities of SKP from four perspectives. |
Enhancing Semantic Consistency of Large Language Models through Model Editing: An Interpretability-Oriented Approach (2024.findings-acl)
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| Challenge: | Large Language Models generate inconsistent and sometimes contradictory outputs when presented with a prompt that has equivalent semantics but is expressed differently from the original prompt. |
| Approach: | They propose to refine a Large Language Model (LLM) with prompt-output pairs with equivalent semantics to achieve semantic consistency. |
| Outcome: | The proposed method improves the semantic consistency and task performance of LLMs. |
Intent Discovery with Frame-guided Semantic Regularization and Augmentation (2023.findings-acl)
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| Challenge: | Existing intent discovery methods focus on transferring prior knowledge of known intents to unknown ones. |
| Approach: | They propose to use frame knowledge as conceptual semantic guidance to bridge the gap between known intents representation learning and unknown intents clustering. |
| Outcome: | The proposed method outperforms solid baselines on two benchmark datasets. |