Infusing Knowledge into Large Language Models with Contextual Prompts
CoRR(2024)
摘要
Knowledge infusion is a promising method for enhancing Large Language Models
for domain-specific NLP tasks rather than pre-training models over large data
from scratch. These augmented LLMs typically depend on additional pre-training
or knowledge prompts from an existing knowledge graph, which is impractical in
many applications. In contrast, knowledge infusion directly from relevant
documents is more generalisable and alleviates the need for structured
knowledge graphs while also being useful for entities that are usually not
found in any knowledge graph. With this motivation, we propose a simple yet
generalisable approach for knowledge infusion by generating prompts from the
context in the input text. Our experiments show the effectiveness of our
approach which we evaluate by probing the fine-tuned LLMs.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要