RAG is a framework. It combines retrieval systems with large language models (LLM). The core concept of RAG is that An LLM should not rely only on its training data.
It should be able to query an external database for knowledge it was not trained on. This knowledge can help it generate or add to its own.
Let’s assume this external database we are talking about is filled with a large chunk of trusted custom data. But, to store this data in the database, we use an embedding model like ChatGPT.
It changes our large chunk of data into vectors. Then, we store those vectors in the external database, which we will now call the vector database. When a user makes a query, we take it and embed it using the same model.
Then, we use similarity search to fetch relevant documents from our database. We then inject them into the context window of the language model.
This is how RAG works. But, we will discuss its inner workings further in this article.
RAG allows large language models (LLMs) to interact with external information. Traditional LLMs rely only on pre-trained knowledge. In contrast, RAG models use external knowledge sources. This makes RAG models more accurate and relevant.
Imagine an LLM as a student preparing for a debate. A traditional LLM only studies textbooks. A RAG model studies books and also researches online, looks in encyclopedias, and talks with experts.
This research is a collaboration.
It allows the RAG model to give more accurate and nuanced information during the debate. It also enables it to provide up-to-date information, much like a well-prepared student.
Why RAG is important?
RAG is not a static system. It serves as a bridge, connecting the vast knowledge of the LLM to the ever-evolving world of information.
When you pose a question to a RAG-powered chatbot about a recent event, it doesn’t rely on what it was trained on years ago. Instead, it actively searches news articles, research papers, and databases for the most current and accurate information, which it then uses to formulate a response.
The significance of RAG stems from the limitations of traditional LLMs. RAG fixes these problems. It does so by basing the LLM’s responses on provable facts from external sources.
Who invented RAG?
RAG’s roots trace back to the early 2000s. Researchers then explored ways to mix retrieval with generation. However, it wasn’t until 2018 that the concept of RAG began to take shape as a distinct framework.
One of the key papers that laid the foundation for RAG is “The Natural Language Decathlon: Multitask Learning as Question Answering” by McCann. It was published in 2018. It did not explicitly use the term “RAG.” But, it introduced a model that combined a retriever to fetch information from Wikipedia. It also used a generator to produce answers to questions. This was a big change. Earlier approaches relied only on retrieval or generation.
Another important paper was published in 2020. It was called “REALM: Retrieval-Augmented Language Model Pre-training,” by Guu. It explicitly introduced the term “Retrieval-Augmented Generation.” This paper presented a model.
The model was pre-trained on a massive text corpus. This enabled it to do many tasks. These include question-answering, fact-checking, and finding documents.
REALM’s success showed that RAG can make better and more flexible language models.
These early papers ignited much interest in RAG. They led to a flurry of research and development in later years. Researchers began exploring new retrieval methods.
They also looked into new generation and training techniques. They did this to improve RAG models. In 2021, RAG in action again made history.
An example was Facebook AI Research’s (FAIR) Dense Passage Retrieval (DPR) system. DPR demonstrated a significant improvement in question-answering accuracy compared to traditional LLMs.
DPR could answer questions that LLMs alone couldn’t. It did this by getting relevant passages from Wikipedia.
The impact of RAG extends beyond research labs into real-world applications. Companies like Cohere and AI21 Labs use RAG to power their language models. RAG provides better solutions. It is for tasks like text summarization, content generation, and customer service. Cohere is partly owned by Patrick Lewis.
Why RAG Matters?
RAG truly shines. It’s like giving our debate students, whom we previously mentioned, access to a library, the Internet, and even experts in real-time. it will let them address the latest topics with confidence. They will do so accurately.
For example, a language model trained on data up to 2021 would need to be made aware of events or discoveries in 2023. This becomes clear when asking about recent events. The LLM might need help to give relevant answers or might make up information.
RAG addresses this challenge by introducing a dynamic element into the model’s knowledge base. RAG doesn’t just rely on static information.
It enables the model to tap into a live knowledge source, like a search engine or a specialized database.
This means the model can access the latest information at the time of the query. This ensures its responses are relevant and accurate.
A notable example of how RAG bridges this gap is in scientific research. Researchers at the Allen Institute for AI (AI2) made a RAG-based system. They called it Semantic Scholar.
It uses a retrieval model to search a vast corpus of scientific papers. It also uses a generation model to summarize the findings for a user’s query. This lets researchers stay up to date on the latest in their field. They can do this even if they need more time to read every paper.
In the business world, RAG is used to enhance customer service chatbots. These chatbots can now access a company’s knowledge base or product documentation.
They use it to give accurate and up-to-date information to customers. This helps them resolve queries more efficiently.
How RAG Works
We must delve into its inner workings. Then, we can truly appreciate RAG’s ability to combat these limits. RAG’s power comes from the synergy of its two core parts: the retrieval system and the big language model (LLM).
The retrieval system is like a librarian in a vast library. It finds relevant documents or passages from a knowledge base based on the user’s query. It’s like a specialized search engine. It understands the nuances of your question. It can pinpoint the exact information you need in a sea of data.
This retrieval process is a complex keyword match. Modern retrieval systems use smart algorithms. They understand the meaning of the query and the documents in the knowledge base.
This allows them to retrieve passages that are not only relevant in terms of keywords but also in terms of context and meaning.
Two main retrieval methods are used in RAG: dense and sparse. Dense retrieval is exemplified by Facebook AI Research’s DPR system. It uses dense vectors of the query and documents to find the most similar passages. This approach is effective in getting the right information. It works for complex or unclear queries.
Sparse retrieval, on the other hand, relies on traditional keyword-based search techniques. Sparse retrieval needs less computation than dense retrieval. But, it may only sometimes capture the query’s nuanced meaning and give less relevant results.
The method used to retrieve answers depends on many factors. These include the size and complexity of the knowledge base. Also, the desired accuracy and the available resources. In practice, a hybrid approach uses both dense and sparse retrieval. It achieves the best of both worlds.
Once the system finds the right documents, the LLM takes over. The LLM is the middleman and communicator. It takes this information and weaves it into a clear response. It’s like a skilled writer who can take disparate pieces of information and craft a compelling narrative.
The RAG Process
Step 1 Query Formulation
The RAG process initiates with query formulation. This crucial step transforms the user’s raw input. It can be a question, a request for information, or a creative prompt. It makes the input into a form that the retrieval system can understand and act upon.
Think of it as translating your query into the language of the knowledge base. You might rephrase a question to make it more searchable on Google. The RAG system processes your query to ensure it fits the retrieval system’s indexing and search.
Next, the query is often turned into a number, such as a vector or a set of keywords. This allows the retrieval system to compare the query to the documents in the knowledge base. It can then find the most similar ones.
The specific method used for this transformation depends on the retrieval technique employed. For example, dense retrieval systems often use neural networks.
They use them to embed the query and documents into a shared vector space. In contrast, sparse retrieval systems might rely on traditional keyword-based representations.
Step 2 Retrieval
With this refined query, the retriever sets out on its mission. It will explore the vast knowledge base.
This knowledge base can take many forms. It can be a collection of structured documents, like Wikipedia articles or research papers.
Or, it can be unstructured data, like social media posts or news feeds. It’s akin to a vast library filled with abundant information waiting to be discovered.
The retriever is guided by the query. It searches a vast knowledge base for documents that match the user’s intent. The search process is far from a simple keyword match. Modern retrievers use math to understand the query and the documents.
They are smart. They ensure that the retrieved information is relevant and fits the user’s needs.
To illustrate, let’s return to the example. It is a query about a recent scientific discovery. A traditional search engine might only return documents with the words “scientific” and “discovery.” This could be too much and not what the user wants.
In contrast, a RAG-based retrieval system would delve deeper. It would analyze the context of the query. It would consider factors such as the field of science. It would also consider the date of the discovery and any other relevant details. This nuanced understanding would let the retriever find documents about the discovery. It would save the user time and effort.
Step 3 Generation
The retrieval augmented generation (RAG) process has found a set of relevant documents. It now moves to its final and most critical stage: generation. This is where the LLM crafts a response. It directly addresses the user’s query. The LLM is armed with the insights from the retrieved information.
Unlike traditional LLMs, which rely solely on their pre-trained knowledge, the RAG LLM is presented with a contextually rich set of information. This lets it make responses. The responses are not just relevant and accurate. They are also complete and detailed.
The generation process is a complex interplay of understanding, synthesis, and articulation. The LLM must first read the retrieved documents. It must extract key points and find relationships.
They must also resolve any contradictions or ambiguities. This requires a deep understanding of language, semantics, and domain-specific knowledge.
Next, the LLM combines the information. It uses insights from many sources to form a clear story. This process is not just spitting out facts. It involves interpreting and combining information.
This creates a response that goes beyond the individual documents. For instance, the LLM might combine a scientific paper with a news article. This mix provides a full answer to a question about a recent discovery.
Finally, the LLM articulates this synthesized knowledge into a clear and concise response. The generated response should be easy to understand, even for users who are not experts in the domain.
It should also be tailored to the specific query and the context of the conversation. For example, a response to a question about a medical condition might be different for a doctor than for a patient.
The generation’s success depends on many factors. These include the quality of the retrieved documents, the LLM’s language skills, and the retriever and LLM’s integration.
When these elements come together well, a powerful RAG system can do many tasks. It can answer complex questions, summarize long documents, and have meaningful conversations.
Applications of RAG
RAG has shown exceptional skill in question answering (QA). This is especially true for tough queries. Those queries need accurate and up-to-date info. For instance, in healthcare, access to the latest medical knowledge is crucial.
RAG-based QA systems are being developed to help clinicians make informed diagnoses and treatment choices. They can analyze lots of medical data. This data includes literature, trial results, and patient records.
They use it to give reliable answers to complex medical questions. This can save lives.
Beyond healthcare, RAG is also transforming the way we summarize information. Traditional summarization techniques often failed with complex, long documents. Their summaries lacked specificity and missed critical details.
RAG can access and combine info from many sources. It is revolutionizing this field. Recent research has focused on using RAG for abstractive summarization. In this, the model makes a short summary that captures the essence of the original document.
This is instead of just taking out sentences word-for-word. This approach has shown promise. It develops clear summaries. They are valuable for researchers, journalists, and students.
RAG is also reshaping the conversational AI landscape. Once limited by pre-set responses, Chatbots are now powered by RAG. This change lets them deliver more helpful and factual interactions.
For example, customer service chatbots can now use a company’s knowledge base or product documentation. They use them to give accurate and up-to-date answers to customer questions.
In information retrieval tasks, RAG-based chatbots can search lots of data to find relevant information. This makes them valuable tools for researchers and analysts.
The potential applications of RAG extend far beyond these examples. RAG can help writers generate ideas. It can also help them research topics and draft articles in content creation. RAG is used in research to automate literature reviews. It identifies knowledge gaps and generates hypotheses.
Limitations of RAG
RAG’s promise is huge. But, its success hinges on solving many key challenges and factors. A key factor is the quality of the knowledge base itself. RAG rests on a solid, accurate, and complete knowledge base.
A weak or flawed foundation is like the foundation of a house. It compromises the whole structure.
The latest insight in this area emphasizes the need for special tools and techniques. They are needed to curate and maintain high-quality knowledge bases for RAG. These tools can automate the process of collecting, cleaning, and organizing information.
They will keep the knowledge base up-to-date, relevant, and error-free. For instance, tools like Snorkel AI were developed by Stanford University.
People use them to create large labeled datasets for training machine learning models. This includes models used in RAG systems.
Ensuring retrieval accuracy is another significant challenge. As we’ve discussed, the retriever finds the most relevant documents.
But, it’s not always easy to be highly accurate. There’s often a trade-off. It’s between recall (finding all relevant documents) and precision (finding only relevant documents). A retriever that prioritizes recall might find many records.
But, some might be irrelevant. A retriever that prioritizes precision might miss some relevant documents to avoid irrelevant ones.
Research aims to develop better algorithms to rank and understand queries. This will improve search accuracy.
For example, researchers at the University of Washington have developed a neural ranking model. It considers the document’s relevance to the query and the quality of the document.
This approach has shown promise. It improves retrieval accuracy, especially for long and complex queries.
Bias and fairness are also major concerns in RAG, as they are in any AI system. The knowledge base itself might contain biases.
These biases reflect the biases of the sources from which it was created. For example, a knowledge base built on historical data might perpetuate historical biases.
The LLM used in RAG might also show biases. The biases depend on its training data and architecture.
Researchers are finding ways to fix bias in RAG. They are in its retrieval and generation phases. For example, researchers at Google Research have developed a method for debiasing word embeddings.
These are numerical representations of words used by LLMs. The researchers removed biases from the embeddings. This reduced gender and racial biases in the LLM’s text.
In the retrieval phase, researchers are creating ways to find biases. They will then fix them in the knowledge base.
For instance, researchers at the University of Massachusetts Amherst have developed a method. It finds biased documents in a knowledge base by analyzing their language.
It compares the language to a reference corpus.
Addressing these challenges is crucial for the continued advancement of RAG. We must build RAG systems on high-quality knowledge bases.
They should use accurate retrieval methods and reduce biases. This will unlock their full potential. It will also ensure they are used responsibly and ethically.
Final Thoughts
Retrieval-augmented generation (RAG) represents a significant leap forward in the evolution of AI.
RAG is changing how we use and interact with information. It does this by blending the vast knowledge of large language models. It can access and process real-time information. RAG is proving its worth in many ways.
It answers complex questions, summarizes long documents, and has meaningful conversations.
It’s a paradigm shift in how we think about AI.
RAG is not just about making smarter machines. It’s about empowering humans to make better decisions. It helps them gain deeper insights and unlock new possibilities.
As we’ve explored, RAG is not without its challenges. Still, ongoing research and development in this field show the way for a future.
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