The Wonders of RAG: Streamlining Knowledge with Advanced Techniques Systematic literature review Report

The Wonders of RAG: Streamlining Knowledge with Advanced Techniques Systematic literature review Report | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 14 March 2025 V1 Latest version Share on The Wonders of RAG: Streamlining Knowledge with Advanced Techniques Systematic literature review Report Authors : Wafa Bazzi 0009-0009-8017-2462 [email protected] , St Wafaa Bazzi , and Mervat Gaith Authors Info & Affiliations https://doi.org/10.22541/au.174197235.57492382/v1 Published Journal of Neurology Research Review & Reports Version of record Peer review timeline 705 views 402 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The RAG addresses the limitations of standard Large Language Models (LLMs) by incorporating external data through Information Retrieval, thereby enhancing their generation ability. As a recent advancement, RAG improves the selection of knowledge sources for response generation in dialogues. Although LLMs generate answers to questions, these answers may sometimes be of suboptimal quality and contain inaccuracies. The RAG framework includes a fine-tuning process that refines models using feedback and examples based on relevance. This process further enhances Open Domain Question Answering by incorporating external data through Information Retrieval. The RAG end2end extension dynamically updates external data during the training of both the retriever and generator, as well as during the training of Dense Passage Retrieval (DPR) models with QA pairs. This process eliminates the need for large continuous improvements in prediction. RAG goes beyond merely creating a smarter ChatGPT; it enables conversations by integrating external sources, adding personalized external sources, and implementing metrics to evaluate these sources, thereby generating beneficial sources. The framework also employs metrics to evaluate answers, refines them through dialogue and feedback, and reduces hallucinations by augmenting with up-to-date knowledge. In summary, these instances highlight the power of RAG and its potential applications for optimizing language models. However, RAG has some limitations. The quality of the generated responses may be impacted by the quality of the incorporated external data. If the data is inaccurate or biased, this could negatively affect the responses. Furthermore, hallucinations remain a challenge because inaccuracies can arise if the input does not contain sufficient information or metrics for evaluation. Future work should focus on enhancing data integration, educating the prompt query, developing real-time correction mechanisms, and adapting RAG for specific domains. Supplementary Material File (final-search-report-of -wonders of rag-follow-templatedocx.pdf) Download 401.46 KB Information & Authors Information Version history V1 Version 1 14 March 2025 Peer review timeline Published Journal of Neurology Research Review & Reports Version of Record 31 Mar 2025 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords dense passage retrieval dialogue system hallucinations large language (llms) open domain question answering realm retrieval augmented generation (rag) Authors Affiliations Wafa Bazzi 0009-0009-8017-2462 [email protected] View all articles by this author St Wafaa Bazzi Department of Computer Sciences, Cairo university View all articles by this author Mervat Gaith Department of Computer Sciences, Cairo university View all articles by this author Funding Information National Science Foundation Metrics & Citations Metrics Article Usage 705 views 402 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Wafa Bazzi, St Wafaa Bazzi, Mervat Gaith. The Wonders of RAG: Streamlining Knowledge with Advanced Techniques Systematic literature review Report. Authorea . 14 March 2025. DOI: https://doi.org/10.22541/au.174197235.57492382/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. 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