Papers by Junzhou Zhao
Distinguish Confusing Law Articles for Legal Judgment Prediction (2020.acl-main)
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| Challenge: | Existing methods to assist legal judgment are limited and can't solve confusing charges issue. |
| Approach: | They propose an end-to-end model to predict a legal judgment based on a textual description of the case and a graph neural network to learn subtle differences between confusing law articles. |
| Outcome: | The proposed model can learn subtle differences between confusing law articles and extract effective discriminative features from fact descriptions. |
The Knowledge Alignment Problem: Bridging Human and External Knowledge for Large Language Models (2024.findings-acl)
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| Challenge: | Large language models often ignore external knowledge to generate accurate answers . despite correct groundings, they can rely on wrong grounding or biases to hallucinate . |
| Approach: | They propose a framework that integrates human and human user clarifications to improve knowledge alignment. |
| Outcome: | The proposed framework improves model performance and mitigates hallucination by producing user-centered clarifications. |