Understanding the Mechanisms: How Retrieval Augmented Generation Keeps LLMs Up to Date

As large language models (LLMs) continue to push the boundaries of natural language processing, a significant challenge arises keeping their knowledge current and relevant in a rapidly evolving world. While traditional language models excel at generating coherent text, their knowledge is limited to the static training data they were exposed to, leaving them ill-equipped to handle dynamic, real-time information needs. Enter retrieval augmented generation (RAG), a novel approach that combines the generative power of LLMs with the ability to retrieve and incorporate relevant information from external data sources during inference. This blog post will delve into the technical mechanisms behind RAG, exploring its architecture, components, and implementation details, accompanied by code examples to illustrate its practical application.

https://docs.aws.amazon.com/sagemaker/latest/dg/jumpstart-foundation-models-customize-rag.html

In RAG, the external information used to augment user queries and prompts can originate from diverse data sources like document repositories, databases, or APIs. Retrieval-Augmented Generation (RAG) optimizes the output of a large language model by integrating references from an authoritative knowledge base beyond its initial training data. Large Language Models (LLMs) are trained on extensive datasets and utilize billions of parameters to generate original content for tasks such as question answering, language translation, and text completion.

The Traditional Language Modeling Paradigm

Before diving into RAG, it's crucial to understand the traditional language modeling paradigm and its limitations. Language models are typically trained on vast datasets of text in a self-supervised manner, learning statistical patterns and accumulating knowledge encoded in that training data. During inference, these models generate text based solely on their internalized knowledge from training, without any external data retrieval. While this approach has yielded impressive results in various natural language tasks, it suffers from several inherent limitations:

Knowledge Staleness: The model's knowledge is frozen at the time the training data was collected, potentially missing critical recent developments or updates.

Knowledge Bottlenecking: With model sizes ranging from billions to trillions of parameters, it becomes infeasible to store all relevant knowledge directly in the model weights.

Single-Shot Learning: Once training is complete, the model cannot easily learn or incorporate new knowledge without undergoing full retraining, which is computationally expensive and time-consuming.

These limitations hinder the ability of LLMs to provide up-to-date and comprehensive information, particularly in rapidly evolving domains or scenarios where real-time data access is crucial.

Retrieval Augmented Generation: Bridging the Gap

RAG addresses these limitations by introducing an additional retrieval component that allows LLMs to query and incorporate relevant information from external data sources during the text generation process. This additional context can help the model stay current, augment its knowledge, and produce more factual and up-to-date outputs. The RAG architecture consists of three main components:

Encoder: This component is responsible for encoding the input query or prompt into a high-dimensional vector representation using the pre-trained LLM's encoder.

Retriever: The retriever component uses the encoded query vector to retrieve relevant documents, passages, or data entries from an external corpus. This can be achieved through various techniques, such as dense vector search or sparse keyword matching.

Decoder: The LLM's decoder attends to the augmented context, which consists of the original query concatenated with the top-k retrieved pieces of information. The decoder then generates the final output text, synthesizing the model's internalized knowledge with the retrieved context.

This architecture allows RAG to leverage the strengths of both LLMs and external data sources, resulting in more up-to-date and comprehensive generated outputs.

Dense Vector Retrieval with FAISS

One of the key components of RAG is the retriever, responsible for efficiently searching and retrieving relevant information from external corpora. A popular approach for implementing this component is through dense vector retrieval, where the query and documents are encoded into high-dimensional vector representations, and similarity search is performed in this vector space.

Here's an example of how to perform dense vector retrieval using PyTorch, FAISS (a library for efficient similarity search), and the pre-trained SentenceTransformers:

In this example, we first load a pre-trained SentenceTransformer encoder, which can encode both the query and passages into high-quality dense vector representations. We then create a FAISS index to efficiently store and search for these dense vectors. After encoding the query and corpus passages, we add the passage vectors to the FAISS index and perform a k-nearest neighbor search to retrieve the top-k most relevant passages for the given query. The output of this code snippet will be:

As you can see, the top retrieved passages are relevant to the query "What is the capital of France?", providing the necessary context to augment the LLM's generation.

Fusion Strategies:

Combining Retrieval and Generation Once the relevant information has been retrieved, the next challenge lies in effectively fusing this retrieved context with the LLM's generation capabilities. Several fusion strategies have been proposed and explored in RAG systems:

Concatenation-based Fusion: In this approach, the retrieved passages are simply concatenated with the original query to form the augmented input context. The LLM's decoder then attends to this augmented context during generation.

Attention-based Fusion: Instead of direct concatenation, this strategy involves using attention mechanisms to dynamically weight and combine the retrieved information with the LLM's internal representations during the decoding process.

For instance, attention-based fusion can be implemented by modifying the LLM's decoder to attend differentially to the retrieved passages and the original prompt during generation:

Here, prompt_vec and retrieved_vec represent the encoded vectors for the prompt and retrieved passages, respectively. The decoder's cross-attention mechanism attends to both sources during generation, allowing for more controlled fusion.

Reranking-based Fusion: Here, the LLM first generates multiple candidate outputs, which are then reranked or rescored based on their compatibility with the retrieved context, allowing for more controlled incorporation of external knowledge.

Reranking-based fusion strategies involve generating multiple candidate outputs and reranking them based on their compatibility with the retrieved context:

In this example, the rerank function would score each candidate output based on its compatibility with the retrieved context vectors retrieved_vec, potentially using techniques like cross-attention or learned scoring functions.

Under the Hood: An Example

To better understand the inner workings of RAG systems, let's walk through an example that illustrates the key components and their interplay when processing a given prompt. Suppose we have a RAG system with a pre-trained language model and a retrieval component connected to a Wikipedia corpus. We'll use the following prompt as an example:

1. Query Encoding
The first step is to encode the input prompt into a high-dimensional vector representation using the pre-trained LLM's encoder:

Here, we use the pre-trained BERT model and tokenizer to encode the prompt into a vector representation query_vec.

2. Dense Vector Retrieval
Next, we use the encoded query vector to retrieve relevant passages from the Wikipedia corpus using dense vector search:

The retrieve function performs efficient nearest neighbor search in the vector space, returning the top-k most relevant passages from the indexed Wikipedia corpus.

3. Context Augmentation
The retrieved passages are then concatenated with the original prompt to form the augmented context for the LLM's decoder:

4. Language Generation
Finally, the LLM's decoder attends to the augmented context and generates the final output text, fusing the model's knowledge with the retrieved information:

This code snippet uses the T5 sequence-to-sequence model for generation, taking the augmented context as input and generating the output text output_text. The generated output will likely be an informative response about the history of the Eiffel Tower's construction, incorporating both the LLM's knowledge and the relevant retrieved passages from Wikipedia.

Scaling RAG Systems

As the scale and complexity of RAG systems grow, several challenges and considerations arise:

Efficient Indexing and Search: With massive external corpora, efficient indexing and retrieval techniques become crucial. Approaches like hierarchical clustering, approximate nearest neighbor search, and learned index structures can help scale billions or trillions of documents.

Retrieval Quality and Relevance: Improving the quality and relevance of retrieved information is an ongoing area of research, with approaches like dense-sparse hybrid retrieval, query reformulation, and iterative retrieval showing promising results.

Architectural Innovations: New model architectures and training strategies are being explored to better integrate retrieval and generation components, enabling more seamless fusion and knowledge grounding.

Factual Consistency and Hallucination Mitigation: As RAG systems become more capable, ensuring factual consistency and mitigating hallucinated or contradictory outputs remains a critical challenge, often addressed through techniques like consistency scoring, fact checking, and iterative refinement.

Ethical Considerations: As with any powerful AI system, RAG systems must be developed and deployed with careful consideration of ethical implications, such as potential biases in the retrieved data, privacy concerns, and responsible use of these technologies.

Conclusion

As language AI continues to advance, techniques like retrieval augmented generation that combine the strengths of large language models and external data access will likely play a pivotal role in building AI assistants with dynamic, ever-growing knowledge. While challenges remain in areas like efficient retrieval, knowledge grounding, and factual consistency, RAG represents a promising paradigm for keeping language models up-to-date and relevant in our rapidly evolving world.

References:
https://docs.aws.amazon.com/sagemaker/latest/dg/jumpstart-foundation-models-customize-rag.html

~ Author: Yasir UI Hadi