Summary of Optimizing Multi-task Learning For Enhanced Performance in Large Language Models, by Zhen Qi et al.
Optimizing Multi-Task Learning for Enhanced Performance in Large Language Models
by Zhen Qi, Jiajing Chen, Shuo Wang, Bingying Liu, Hongye Zheng, Chihang Wang
First submitted to arxiv on: 9 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study investigates a novel approach to improve large language models like GPT-4 by leveraging the multi-task learning framework. The proposed method combines shared feature extractors with task-specific modules, enabling knowledge-sharing and optimization across multiple tasks. Experiments are conducted on two benchmark tasks: text classification and automatic summary generation using the GLUE dataset. The results demonstrate that the multi-task model outperforms single-task GPT-4, GPT-3, BERT, and Bi-LSTM with Attention in both accuracy and ROUGE value. This suggests improved generalization ability and collaborative learning between tasks. The study also highlights good learning efficiency and adaptability to the test set. The proposed framework verifies its applicability in large language models, particularly in balancing different tasks. Future directions include integrating multimodal data and dynamic task adjustment technology to further advance practical applications across fields. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study explores a new way to make language models better by combining multiple tasks together. They tested this idea on two tasks: classifying text and summarizing it. The results show that their method works better than other approaches in both tasks. This is important because it can help language models learn more generally and work well with different types of data. The study also shows that the model learns quickly and accurately. This new approach has the potential to make language models more useful in many areas, such as healthcare, finance, and education. |
Keywords
» Artificial intelligence » Attention » Bert » Generalization » Gpt » Lstm » Multi task » Optimization » Rouge » Text classification
