A graph-based approach to closed-domain natural language generation

Научный результат. Серия: Вопросы теоретической и прикладной лингвистики. 2024. Vol. 10. No. 3. P. 135–167.

Victoria I. Firsanova

Graph-based Natural Language Processing (NLP) methods have seen significant advancements in recent years with the development of Large Language Models (LLMs) and Retrieval Augmented Generation (RAG). LLMs are sophisticated models that recognize numerous NLP tasks by analyzing the users' natural language instructions called prompts. However, their industrial use is questionable due to such ethical concerns as false information generation called hallucinations, high risks of data breaches, and plagiarism. The paper introduces a novel NLP architecture, the Graph-Based Block-to-Block Generation (G3BG), which leverages state-of-the-art deep learning techniques, the power of attention mechanisms, distributional semantics, graph-based information retrieval, and decentralized networks. The model encodes user prompts to mitigate data breach risk, retrieves relevant information from a graph knowledge base, and forms a block for a conditional language model using LLMs to perform a new secure type of RAG...

Keywords: Генеративный искусственный интеллект, Large Language Models, Language Generation, Language Understanding, Generative Artificial Intelligence, Decentralized Networks, Data Encoding, Distributional Semantics, Closed-Domain Systems, Генерация естественного языка, Понимание естественного языка, Большие языковые модели, Децентрализованные сети, Кодирование данных, Дистрибутивная семантика, Закрытая предметная область