In recent years, artificial intelligence has witnessed a remarkable transformation, primarily driven by the arrival of large language models (LLMs). LLMs have unlocked a world of possibilities in natural language processing (NLP), enabling applications ranging from automated content creation to chatbots and virtual assistants.
While these models have showcased impressive text generation capabilities, they grapple with a central challenge: producing content that’s not just coherent but also contextually accurate and grounded in real-world knowledge. This limitation is especially troublesome in contexts where precision and factual correctness are paramount.
To address this challenge, a cutting-edge approach has emerged: retrieval-augmented generation (RAG). Building upon the strengths of GPT and similar models, RAG seamlessly integrates information retrieval capabilities. This integration empowers generative AI systems to access and incorporate knowledge from vast external sources, such as databases and articles, into the text generation process.
This fusion of natural language generation and information retrieval opens up new horizons in AI-powered text generation. It bridges the gap between pure generative models and external knowledge, promising enhanced contextual relevance and factual accuracy. In this exploration, we will delve deeper into RAG, its underlying principles, real-world applications, and the profound impact it may have on how we interact with generative AI systems and create human-like text.
What is retrieval augmented generation (RAG)?
Retrieval Augmented Generation (RAG) combines the advanced text-generation capabilities of GPT and other large language models with information retrieval functions to provide precise and contextually relevant information. This innovative approach improves language models' ability to understand and process user queries by integrating the latest and most relevant data. As RAG continues to evolve, its growing applications are set to revolutionize AI efficiency and utility.
In general, large language models are really good at doing many natural language processing tasks. Their generated text is sometimes straight to the point, accurate, and just what the user needs. But a lot of the time, this isn’t the case.
You've most probably encountered a situation where you ask a question to ChatGPT, and you feel like something is wrong with the output it generated, no matter how confident the model seems to be. Then you go to check the information yourself and find out that GPT actually "lied." This phenomenon of large language models is called hallucination. Let's think about why this happens.
General-purpose language models are pre-trained on vast amounts of data from everywhere. But this doesn't mean that it knows the answer to every question. General LLMs fall short in cases like up-to-date or relevant information, domain-specific context, fact-checking, etc. That's why they're called general-purpose and need the assistance of other techniques that are widely implemented to make LLMs more versatile.
2020, Meta, RAG model
In 2020, Meta researchers came up with a paper introducing one of such "assisting" techniques – retrieval augmented generation (RAG). At its core, RAG is an innovative technique that merges the capabilities of natural language generation (NLG) and information retrieval (IR).
The fundamental idea behind RAG is to bridge the gap between the vast knowledge in general-purpose language models and the need for precise, contextually accurate, and up-to-date information. While general LLMs are powerful, they are not infallible, especially in scenarios that demand real-time data, domain-specific expertise, or fact-checking.
How does retrieval augmented generation (RAG) work?
RAG is about feeding language models with necessary information. Instead of asking LLM directly(like in general-purpose models), we first retrieve the very accurate data from our knowledge library that is well maintained and then use that context to return the answer. When the user sends a query(question) to the retriever, we use vector embeddings(numerical representations) to retrieve the requested document. Once the needed information is found from the vector databases, the result is returned to the user. This largely reduces the possibility of hallucinations and updates the model without retraining the model, which is a costly process. Here's a very simple diagram that shows the process.
RAG brings together four key components:
- Embedding model: This is where documents are turned into vectors, or numerical representations, which make it easier for the system to manage and compare large amounts of text data.
- Retriever: Think of this as the search engine within RAG. It uses the embedding model to process a question and fetch the most relevant document vectors that match the query.
- Reranker (optional): This component takes things a step further by evaluating the retrieved documents to determine how relevant they are to the question at hand, providing a relevance score for each one.
- Language model: Finally, this part of the system takes the top documents provided by the retriever or reranker, along with the original question, and crafts a precise answer.
In practical terms, RAG is mainly famous in applications that require up-to-date and contextually accurate content. It bridges the gap between general language models and external knowledge sources, paving the way for improved content generation, question-answering, personalized recommendations, and more.
LLM & RAG use cases
Retrieval augmented generation, the new hot topic in large language models, is used in many LLM applications. Let's discuss a few cases discussed in SuperAnnotate's webinar with Databricks.
Databricks LLM-powered chatbot
During the webinar, we explored how Databricks is pioneering the use of large language models (LLMs) in creating advanced documentation chatbots. These bots are designed to simplify the search for information by providing direct access to relevant documents.
Intelligent document retrieval
The chatbot serves as a dynamic assistant, offering immediate responses to user inquiries about various features, such as deploying Spark for data processing. With RAG, the chatbot efficiently pulls the appropriate document from the Spark knowledge repository in response to a question. This strategy ensures that users obtain accurate and pertinent documentation, facilitating an effective and user-friendly learning experience.
Personalized user experience with enhanced language models
Databricks' use case extends into personalized information retrieval, harnessing the full potential of LLMs. By doing so, the system not only provides general documentation but also adapts its responses to fit the user's specific needs, paving the way for a revolution in user support interactions.
Evaluating the effectiveness of LLMs
A pivotal discussion during the webinar addressed the challenge of evaluating LLM effectiveness. Assessing these models is difficult due to the subjective nature of testing and the diverse range of user experiences. Despite these challenges, it remains crucial to maintain consistent and standardized evaluation practices. A comprehensive feedback collection from customer interactions is essential to refine and validate the model's performance – and SuperAnnotate helped Databricks achieve this.
SuperAnnotate's role in streamlining evaluations
The collaboration between Databricks and SuperAnnotate has introduced an innovative angle to the evaluation spectrum. SuperAnnotate assists Databricks in standardization – cutting down the time and costs associated with LLM evaluations.
Deploying LLMs as initial evaluators can delegate routine judgment tasks to the generative AI, reserving more complex decision-making for human experts. Instead of humans annotating data for LLMs (evaluating LLM results), AI does that. This process is a relatively new topic in AI and is called reinforcement learning from AI feedback (RLAIF). It’s an alternative to the famous reinforcement learning from human feedback (RLHF). This approach promotes a more effective distribution of tasks, where LLMs are used as evaluators instead of humans, ensuring that human intellect is applied in more complex and nuanced areas. It underscores a strategic collaboration where generative AI and human expertise work together to achieve superior evaluation standards in various LLM use cases.
RAG business value
It’s no secret that most enterprises today consider integrating language models into their business operations. Retrieval augmented generation changed the way businesses handle information and customer queries. By integrating the retrieval of specific information with the generative capabilities of language models, RAG provides precise, context-rich answers to complex questions. This integration brings value to businesses in several ways.
Accurate information: RAG ensures a high degree of accuracy in responses. Since the system first retrieves information from a reliable database before generating an answer, it minimizes the risk of providing incorrect or irrelevant information. This can be particularly beneficial for customer service platforms, where accurate information is crucial for maintaining customer trust and satisfaction.
Recourse efficiency: RAG enhances the efficiency of information retrieval, saving time for both employees and customers. Instead of sifting through databases or documents manually, users can receive instant access to the information they need. This rapid delivery of knowledge not only improves the user experience but also frees up employee time for other critical tasks.
Knowledge efficiency: RAG ensures that responses are matched with the most up-to-date information and relevant documentation, and businesses can maintain a high standard of information dissemination. This is vital in fields like tech and finance, where outdated information can lead to significant errors or compliance issues.
RAG vs fine-tuning
Retrieval augmented generation and LLM fine-tuning, although having similar objectives, are two different techniques for optimizing large language model performances. Let's discuss the differences and understand when to choose RAG vs. fine-tuning.
The chart gives a detailed comparison between RAG and fine-tuning based on different criterias.
Fine-tuning involves additional training data stages for a large language model on new datasets to refine its performance for particular functions or knowledge areas. This specificity means while a model becomes more adept in certain scenarios, it may not maintain its effectiveness across unrelated tasks.
In contrast, RAG empowers LLMs and LLM agents by dynamically enriching them with updated, relevant information from external databases. This method boosts the model's capability to answer questions and provide timely, pertinent, and context-aware responses. While this sounds catchy, there's always a trade-off in increased computational demands and possibly extended response times due to the added complexity of integrating fresh information.
One particular advantage RAG has over fine-tuning lies in information management. Traditional fine-tuning embeds data into the model's architecture, essentially 'hardwiring' the knowledge, which prevents easy modification. On the other hand, vector storage used in RAG systems permits continuous updates, including the removal or revision of data, ensuring the model remains current and accurate.
It's worth to mention that RAG and fine-tuning can also be used together to improve LLMs performance. Particularly, if a component of a RAG system has defects, fine-tuning can be used to tackle that issue. This is especially the case when you want your model to excel at a specific task.
Retrieval augmented generation vs semantic search
Another technique used to enhance large language model performance is semantic search. Unlike traditional search methods that rely heavily on keyword matching, semantic search delves into the contextual meaning of the terms used in a query, offering a more nuanced and precise retrieval of information.
Let's consider the limitations of basic search functionality using an everyday scenario. If someone uses a generative AI system to find information about apple cultivation areas, the system might typically look for instances where the words "apple" and "cultivation" appear in its database. This could lead to a mixture of relevant and irrelevant results, like bringing up documents about apple products or cultivation practices unrelated to apples, because the keyword search isn’t literal. Plus, it might overlook articles on specific regions known for apple farming if they don't include the exact phrase the user searched for.
Semantic search improves upon this by grasping the essence behind a user's inquiry. It understands that the user is interested in locations where apples grow, not in general agricultural methods or the company Apple. By interpreting the query's intent and the contextual meaning within source material, semantic search can accurately pinpoint information that matches the user's actual needs. In the context of RAG, semantic search acts as a sophisticated lens, focusing the LLM's broad capabilities on finding and utilizing the most relevant data to answer a question. It filters information through a layer of comprehension, ensuring that the AI system's generative responses aren’t only accurate but also contextually grounded and informative.
Wrapping up
The collaboration of vast language models like GPT with retrieval techniques represents a significant stride toward more intelligent, aware, and helpful generative AI. With RAG, we're dealing with a system that understands context, digs out relevant, up-to-date information, and presents it in a cohesive manner. Being one of the most significant and promising techniques for making LLMs more efficient, the practical uses of RAG are just beginning to be tapped into, with future developments set to enhance its applications even further.
