Papers by Zi Yu
CoBa: Convergence Balancer for Multitask Finetuning of Large Language Models (2024.emnlp-main)
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| Challenge: | Existing multi-task learning approaches for large language models fall short due to computational intensive or lack of simultaneous task convergence. |
| Approach: | They propose a new multi-task learning approach that dynamically adjusts task weights during the training process, ensuring that the validation loss of all tasks progresses towards convergence at an even pace. |
| Outcome: | The proposed approach improves the performance of large language models by up to 13% compared to the second-best approaches. |
ForestCast: Open-Ended Event Forecasting with Semantic News Forest (2025.findings-emnlp)
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| Challenge: | Existing approaches and datasets overlook the complex relationships among events . current research lacks comprehensive evaluation methods to evaluate OEEF . |
| Approach: | They propose a prediction pipeline that extracts forecast-relevant events from news data . forestcast organizes news events into a story tree and predicts subsequent events along each path . |
| Outcome: | The proposed pipeline extracts forecast-relevant events from news data and predicts subsequent events along each path. |
SOM-NCSCM : An Efficient Neural Chinese Sentence Compression Model Enhanced with Self-Organizing Map (2021.emnlp-main)
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| Challenge: | Sentence Compression (SC) is an important natural language processing task . it aims to shorten sentences while preserving the original meanings of the words . improvements on Chinese SC models are still lacking due to several difficulties . |
| Approach: | They propose a neural Chinese SC model enhanced with a Self-Organizing Map from Chinese colloquial sentences from a real-life question answering system. |
| Outcome: | The proposed model achieves a promising F1 score of 89.655 and BLEU4 score of 70.116 . it improves the performance of the whole neural Chinese SC model in a valid manner . |