Papers by Qinghua Chai
Improving Sequential Model Editing with Fact Retrieval (2023.findings-emnlp)
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| Challenge: | Existing methods to fix erroneous knowledge in Pre-trained Language models experience a performance decline when the number of edits increases. |
| Approach: | They propose a framework that leverages factual information to enhance editing generalization and guide the identification of edits by retrieving related facts from the fact-patch memory. |
| Outcome: | The proposed framework can improve model generalization and accuracy even with thousands of edits. |
Inference Helps PLMs’ Conceptual Understanding: Improving the Abstract Inference Ability with Hierarchical Conceptual Entailment Graphs (2024.emnlp-main)
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| Challenge: | Existing approaches to abstract inference ignore the *polysemy* and *hierarchical nature of concepts* . prevailing approaches disregard how arguments might entail differently across various concept levels, thereby missing potential enlargement connections. |
| Approach: | They propose a framework that organizes arguments hierarchically and delves into entailment relations at diverse concept levels. |
| Outcome: | The proposed framework improves the model's generalization and reasoning prowess in natural language inference. |
A Knowledge-Guided Framework for Frame Identification (2021.acl-long)
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| Challenge: | Existing frameworks for frame identification are limited to only a few types of frame knowledge. |
| Approach: | They propose a Knowledge-Guided Frame Identification framework that integrates frame knowledge to learn better frame representation. |
| Outcome: | The proposed framework outperforms the state-of-the-art methods on two benchmark datasets. |