Retrieval-Augmented Generation (RAG) Presentation PDF

Retrieval-Augmented Generation (RAG) Dr. Emad AL Sukhni  ‫مجد العمري‬ ‫تقى غزالن‬ ‫‪Team‬‬ ‫عبير عليمات‬ ‫‪member‬‬ ‫تسنيم حواري‬ ‫ديما عزام‬ Outline  1. Problem Statement  2. Proposed Solution: Retrieval-Augmented Generation (RAG)  3. How RAG Works ...

Retrieval-Augmented Generation (RAG) Dr. Emad AL Sukhni  ‫مجد العمري‬ ‫تقى غزالن‬ ‫‪Team‬‬ ‫عبير عليمات‬ ‫‪member‬‬ ‫تسنيم حواري‬ ‫ديما عزام‬ Outline  1. Problem Statement  2. Proposed Solution: Retrieval-Augmented Generation (RAG)  3. How RAG Works  4. RAG Architecture  5. Applications of RAG  6. Challenges in Implementing RAG Prerequisite Terms Up To Date Information!!!! Solution Traditionally, neural networks are adapted to domain-specific or proprietary information by fine-tuning the model. Although this technique is effective, it is also compute-intensive, expensive, and requires technical expertise, making it less agile to adapt to evolving information. In 2020, Lewis et al. proposed a more flexible technique called Retrieval- Augmented Generation (RAG) in the paper Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. In this paper, the researchers combined a generative model with a retriever module to provide additional information from an external knowledge source that can be updated more easily. In simple terms, RAG is to LLMs what an open-book exam is to humans.  Retrieval-  An overview of optimizing large Augmented language models (LLMs) using external knowledge bases to Generation enhance response accuracy and relevance. (RAG)  What is Retrieval-Augmented Generation (RAG)? Traditional Language RAG Feature Models Pulls external data during response Data Source Relies solely on pre-trained data. generation. Response Limited by the training dataset, Enhanced by retrieving relevant, Accuracy may lack depth or specificity. up-to-date information. Cannot adapt to new or unseen Dynamically retrieves information to Adaptability information after training. address specific queries. Fixed knowledge base from training Accesses large, external datasets Knowledge Base phase. or knowledge bases. Use Case Suitable for general or common Ideal for queries requiring updated Suitability queries. or specific knowledge. The RAG architecture  It is a sophisticated system designed to enhance the capabilities of large language models by combining them with powerful retrieval mechanisms.  It’s essentially a two-part process involving a retriever component and a generator component. Let’s break down each component and their roles in the overall process: The RAG architecture Retriever Component: Function: The retriever’s job is to find relevant documents or pieces of information that can help answer a query. It takes the input query and searches a database to retrieve information that might be useful for generating a response.  Types of Retrievers: Dense Retrievers: These use neural Sparse Retrievers: These rely on network-based methods to create term-matching techniques like TF- dense vector embeddings of the IDF or BM25. They excel at finding text. They tend to perform better documents with exact keyword when the meaning of the text is matches, which can be more important than the exact particularly useful when the query wording since the embeddings contains unique or rare terms. capture semantic similarities.  Generator Component: Function: The generator is a language model that produces the final text output. It takes the input query and the contexts retrieved by the retriever to generate a coherent and relevant response. Interaction with Retriever: The generator doesn’t work in isolation; it uses the context provided by the retriever to inform its response, ensuring that the output is not just plausible, but also rich in detail and accuracy. The workflow of a Retrieval- Augmented Generation (RAG) system Query Processing: It all starts with a query. This could be a question, a prompt, or any input that you want the language model to respond to. Embedding Model: The query is then passed to an embedding model. This model converts the query into a vector, which is a numerical representation that can be understood and processed by the system. Vector Database (DB) Retrieval: The query vector is used to search through a vector database. This database contains precomputed vectors of potential contexts that the model can use to generate a response. The system retrieves the most relevant contexts based on how closely their vectors match the query vector. Retrieved Contexts: The contexts that have been retrieved are then passed along to the Large Language Model (LLM). These contexts contain the information that the LLM uses to generate a knowledgeable and accurate response. LLM Response Generation: The LLM takes into account both the original query and the retrieved contexts to generate a comprehensive and relevant response. It synthesizes the information from the contexts to ensure that the response is not only based on its pre-existing knowledge but is also augmented with specific details from the retrieved data. Final Response: Finally, the LLM outputs the response, which is now informed by the external data retrieved in the process, making it more accurate and detailed. Choosing a Retriever: The choice between dense and sparse retrievers often depends on the nature of the database and the types of queries expected. Dense retrievers are more computationally intensive but can capture deep semantic relationships, while sparse retrievers are faster and better for specific term matches. Hybrid Models: Some RAG systems may use hybrid retrievers that combine dense and sparse techniques to balance the trade-offs and take advantage of both methods Applications of RAG: Enhancing Chatbots and Conversational Agents Question-Answering Systems: Benefits of Using RAG in Various Fields: Healthcare: RAG-powered systems can assist medical professionals by pulling in information from medical journals and patient records to suggest diagnoses or treatments that are informed by the latest research. Customer Service: By retrieving company policies and customer histories, RAG allows service agents to offer personalized and accurate advice, improving customer satisfaction. Education: Teachers can leverage RAG-based tools to create custom lesson plans and learning materials that draw from a broad range of educational content, providing students with diverse perspectives. Challenges in Implementing RAG: Complexity: Combining retrieval and generation processes adds complexity to the model architecture, making it more challenging to develop and maintain. Scalability: Managing and searching through large databases efficiently is difficult, especially as the size and number of documents grow. Latency: Retrieval processes can introduce latency, impacting the response time of the system which is critical for applications requiring real-time interactions, like conversational agents. Synchronization: Keeping the retrieval database up-to-date with the latest information requires a synchronization mechanism that can handle constant updates without degrading performance. Data Dependency and Retrieval Sources: Source Reliability: It’s critical to ensure that the sources of information are reliable and authoritative, especially for applications like healthcare and education. Privacy and Security: When dealing with sensitive information, such as personal data or proprietary content, there are significant concerns around data privacy and security. THE END THANK YOU  Reference 1. Lewis, P., Oguz, B., Rinott, R., Riedel, S., & Stenetorp, P. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. Advances in Neural Information Processing Systems, 33, 9459–9474.  Available at: https://proceedings.neurips.cc/paper/2020/file/6b493230205f780e1bc26945df7481e5- Paper.pdf 2. Izacard, G., & Grave, E. (2021). Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering. arXiv preprint arXiv:2007.01282.  Available at: https://arxiv.org/abs/2007.01282 3. Chen, D., Fisch, A., Weston, J., & Bordes, A. (2017). Reading Wikipedia to Answer Open- Domain Questions. Association for Computational Linguistics (ACL).  Available at: https://aclanthology.org/P17-1171/ 4. Karpukhin, V., Oguz, B., Min, S., Lewis, P., Wu, L., Edunov, S., Chen, D., & Yih, W.-T. (2020). Dense Passage Retrieval for Open-Domain Question Answering. arXiv preprint arXiv:2004.04906.  Available at: https://arxiv.org/abs/2004.04906 5. Yih, W.-T., Chang, M., Meek, C., & Pastalkova, E. (2014). Question Answering Using Knowledge Base with Embeddings. arXiv preprint arXiv:1412.2777.  Available at: https://arxiv.org/abs/1412.2777