PASTA: A Paraphrasing And Self-Training Approach for Knowledge Updating in LLMs
Knowledge updating in pre-trained Large Language Models (LLMs) remains an important challenge. While continual training provides a potential avenue for knowledge updating, it continues to present substantial technical difficulties. Furthermore, LLMs often struggle with accurately answering questions about specific factual information, such as news articles - a capability limitation widely recognized in the research community. This paper proposes PASTA, a simple yet powerful framework for integrating detailed factual information from news articles as new knowledge into LLMs, with the primary goal of building specialized models that accurately answer questions about this knowledge. Our fram...
| Authors | Takayuki Yamamoto, Daisuke Kawahara |
|---|---|
| Primary category | cs.CL |
| Published | 27 June 2026 |
| Last updated at source | 27 June 2026 |
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