RAG
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Developing a RAG-PDF Reader Using Instructor XL and Falcon 7B
Abstract: This study outlines the development of a Retrieval-Augmented Generation (RAG) application, designed to efficiently extract, retrieve, and synthesize insightful responses from complex PDF documents. Leveraging advanced models like Instructor XL for generating high-quality semantic embeddings and Falcon 7B for sophisticated language generation, this system provides a robust solution for document comprehension in academic, research, and professional environments. By implementing efficient PDF text processing, embedding storage with FAISS for rapid similarity-based …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 1, 2025 · pp. 45–49 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
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Automated Math Solver Assist with LLM RAG
Abstract: The challenges of solving complex mathematical problems often hinder efficiency in various scientific and engineering domains. This project proposes an innovative solution to these challenges by integrating automated math solvers with large language model (LLM) retrieval-augmented generation (RAG). The proposed system aims to streamline mathematical problem-solving processes, offering a robust and precise tool for real-time recognition, classification, and solution generation. This work provides a novel method of automating the solution …
Published in Current Trends in Signal Processing · Vol. 14, Issue 2, 2024 · pp. 25–30 Read article