The RAG model is designed to enhance the capabilities of natural language processing models by combining elements of retrieval-based methods with generative approaches.
You might know that the RAG method came after large language model-based generative approaches gained much momentum. So the question is why we need one more method and what specific problem it solves when large language models already exist.
Let’s understand the limitations in generative approaches, what new RAG brings, and ultimately the methodology behind this new architecture named RAG.
I will start with Generative approaches and slowly move towards RAG architecture.
Generative Approaches: Generative methods involve generating responses or content based on learned patterns and training data. These methods are effective for creative tasks and open-ended language generation.
Though generative approaches solve many easy-complex problems and transform industries and human lives, these approaches still lack in different ways and occasions:
Limitations of Large language model-based generative AI approaches:
- Not Updated to the latest information: Generative AI uses large language models to generate texts and these models have information only to date they are trained. For example, if a model is trained on data till Jan 2022 and you ask a question about any event or technological advancement which happened recently after 2022 then it will return inaccurate results or it will tell you that it does not have enough information like below:
- Hallucinations: Hallucinations refer to the output which is factually incorrect or nonsensical. However, the output looks coherent and grammatically correct. This information could be misleading and could have a major impact on business decision-making.
- Domain-specific most accurate information: Generative AI output lacks accurate information many times when specificity is more important than generalized output. For example, organizations have some specific HR policies which are specific to their employees and different from the global rules and policies. Then it becomes difficult to get answers from LLM-based generative AI outputs because those outputs will be more generic rather specific.
- Source Citations: In Generative AI responses, we don’t know what source it is referring to generate a particular response. So citations become difficult and sometimes it is not ethically correct to not cite the source of information and give due credit.
- Updates take Long training time: information is changing very frequently and if you think to re-train those models with new information it requires huge resources and long training time which is a computationally intensive task.
Above are the most important limitations of generative approaches which need solutions as these responses are being used in major decision making and have huge impact.
Now let us understand what is Retrieval Augmentation Generation (RAG) methodology and how it helps to minimize the limitations of generative approaches and complement them to generate more accurate results.
RAG complements generative methods by combining the strength of retrieval-based methods with generative methods. So what are retrieval-based methods?
Retrieval-based methods: Retrieval-based methods involve retrieving information from a predefined set of documents or knowledge sources. These methods are effective for providing factual and contextually relevant information.
Now let’s understand RAG Architecture:
Retrieval-augmented generation, or RAG, was first introduced in a 2020 research paper published by Meta (then Facebook). RAG is an AI framework that allows a generative AI model to access external information not included in its training data or model parameters to enhance its responses to prompts.
- RAG seeks to combine the strengths of both retrieval-based and generative methods.
- It typically involves using a retriever component to fetch relevant passages or documents from a large corpus of knowledge.
- The retrieved information is then used to augment the generative model’s understanding and improve the quality of generated responses.
Key Components:
- Retriever: The retriever component is responsible for efficiently identifying and extracting relevant information from a vast amount of data. It uses methods like dense retrieval or sparse retrieval to retrieve relevant passages.
- Ranking: There is often a ranking mechanism to prioritize the retrieved passages based on their relevance, ensuring that the most pertinent information is used by the generator.
- Generator: The generator is a natural language generation model that takes into account the retrieved information to produce coherent and contextually relevant responses.
Advantages:
- Factual Accuracy: By incorporating retrieval-based methods, RAG models can offer more accurate and contextually relevant information, making them suitable for tasks like question answering.
- Creative Generation: The generative component allows for creative and diverse responses, enabling the model to handle a wide range of language generation tasks.
Applications:
- RAG models are particularly useful in open-domain question-answering systems where the goal is to provide accurate and informative responses to user queries.
For detailed explanation, you may refer the below YouTube Video.
Data Science Duniya 