تقرير
Knowledge Rumination for Pre-trained Language Models
| العنوان: | Knowledge Rumination for Pre-trained Language Models |
|---|---|
| المؤلفون: | Yao, Yunzhi, Wang, Peng, Mao, Shengyu, Tan, Chuanqi, Huang, Fei, Chen, Huajun, Zhang, Ningyu |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Information Retrieval, Computer Science - Machine Learning |
| الوصف: | Previous studies have revealed that vanilla pre-trained language models (PLMs) lack the capacity to handle knowledge-intensive NLP tasks alone; thus, several works have attempted to integrate external knowledge into PLMs. However, despite the promising outcome, we empirically observe that PLMs may have already encoded rich knowledge in their pre-trained parameters but fail to fully utilize them when applying them to knowledge-intensive tasks. In this paper, we propose a new paradigm dubbed Knowledge Rumination to help the pre-trained language model utilize that related latent knowledge without retrieving it from the external corpus. By simply adding a prompt like "As far as I know" to the PLMs, we try to review related latent knowledge and inject them back into the model for knowledge consolidation. We apply the proposed knowledge rumination to various language models, including RoBERTa, DeBERTa, and GPT-3. Experimental results on six commonsense reasoning tasks and GLUE benchmarks demonstrate the effectiveness of our proposed approach, which proves that the knowledge stored in PLMs can be better exploited to enhance performance. Code is available in https://github.com/zjunlp/knowledge-rumination. Comment: EMNLP 2023 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2305.08732 |
| رقم الأكسشن: | edsarx.2305.08732 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |