Oct 7, 2024 · The results indicate that models employing ICL can quickly grasp deep patterns and significantly improve accuracy. In contrast, fine-tuning, ...

This paper presents a counterintuitive finding: For tasks with implicit patterns, ICL captures these patterns significantly better than fine-tuning.

Oct 7, 2024 · In conclusion, our research demonstrates that In-Context Learning (ICL) significantly outperforms fine-tuning in capturing implicit patterns ...

Nov 12, 2024 · The results indi- cate that models employing ICL can quickly grasp deep patterns and significantly improve accuracy. In contrast, fine-tuning, ...

Deeper Insights Without Updates: The Power of In-Context Learning Over Fine-Tuning. Anonymous ACL submission. Abstract. Fine-tuning and in-context learning (ICL) ...

Nov 30, 2024 · Deeper Insights Without Updates: The Power of In-Context Learning Over Fine-Tuning ; Qingyu Yin ; Xuzheng He ; Fan Wang.

Fine-tuning and in-context learning (ICL) are two prevalent methods in imbuing large language models with task-specific knowledge. It is commonly believed that ...

Oct 7, 2024 · The results suggest that in-context learning enables deeper model understanding and broader generalization compared to fine-tuning. Plain ...

Fine-tuning and in-context learning (ICL) are two prevalent methods in imbuing large language models with task-specific knowledge. In-Context Learning. 3.

Co-authors ; Deeper Insights Without Updates: The Power of In-Context Learning Over Fine-Tuning. Q Yin, X He, L Deng, CT Leong, F Wang, Y Yan, X Shen, Q Zhang.