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        <title>Infusing Knowledge into Large Language Models with Contextual Prompts</title>
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    <abstract>Knowledge infusion is a promising method for enhancing Large Language Models for domainspecific 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.</abstract>
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%0 Conference Proceedings
%T Infusing Knowledge into Large Language Models with Contextual Prompts
%A Kinshuk, Vasisht
%A Balaji, Ganesan
%A Vikas, Kumar
%A Vasudha, Bhatnagar
%Y Jyoti, D. Pawar
%Y Sobha, Lalitha Devi
%S Proceedings of the 20th International Conference on Natural Language Processing (ICON)
%D 2023
%8 December
%I NLP Association of India (NLPAI)
%C Goa University, Goa, India
%F kinshuk-etal-2023-infusing
%X Knowledge infusion is a promising method for enhancing Large Language Models for domainspecific 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.
%U https://aclanthology.org/2023.icon-1.65
%P 657-662

Markdown (Informal)

[Infusing Knowledge into Large Language Models with Contextual Prompts](https://aclanthology.org/2023.icon-1.65) (Kinshuk et al., ICON 2023)

• Infusing Knowledge into Large Language Models with Contextual Prompts (Kinshuk et al., ICON 2023)

ACL

• Vasisht Kinshuk, Ganesan Balaji, Kumar Vikas, and Bhatnagar Vasudha. 2023. Infusing Knowledge into Large Language Models with Contextual Prompts. In Proceedings of the 20th International Conference on Natural Language Processing (ICON), pages 657–662, Goa University, Goa, India. NLP Association of India (NLPAI).