retrieval augmented generation
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Retrieval Augmented Generation for Question Answering in Financial Documents
Abstract: In recent years, the integration of Question Answering (QA) with the Retrieval Augmented Generation (RAG) system has transformed to interact with numerous documents. It uses Natural Language Processing (NLP) techniques to improve accuracy and relevant responses derived from huge documents. RAG integrates the advantages of the retrieval and generation process, which allows systems to generate natural responses and extract context from multiple sources. The main reason to use RAG is …
Published in Journal of Advanced Database Management & Systems · Vol. 12, Issue 2, 2025 · pp. 62–68 Read article
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Generative Artificial Intelligence with Emphasis on Large Language Models: Review and Current Trends
Abstract: Generative Artificial Intelligence deals with AI systems that generate new content, such as text, and images. It accomplishes this by using data patterns of texts and images that already exist. Generative AI began an era of major advancement in AI, producing more refined and human-like results. Large Language Models, LLMs, is a part of Generative AI with applications in Natural Language Processing such as text generation, translation, summarization, sentiment detection …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 1, 2025 · pp. 40–46 Read article
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Unmasking Hallucinations in Large Language Models Using Analysis of the LLAMA 2 Model and RAG Intervention
Abstract: The study describes the creation of a chatbot for financial trading called "TradeBot" and how it uses Retrieval Augmented Generation (RAG) to overcome the problem of producing false or unverifiable information, sometimes known as hallucinations. RAG allows the chatbot to refer to an external data source in addition to its taught knowledge, which increases the accuracy of its responses. The NCFM (NSE's Certification in Financial Markets) book was integrated as …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 1, 2025 · pp. 76–86 Read article