RAG is an AI Framework that integrates large language models (LLMs) with external knowledge retrieval to enhance accuracy and transparency. Pre-trained language models generate text based on patterns in their training data. RAG supplements their capabilities by retrieving relevant facts from constantly updated knowledge bases. This grounds the language model's predictions on factual information rather than just its encoded patterns.
By conditioning generations on accurate external context, RAG frameworks improve the reliability of language model outputs. The retrieval mechanism also provides visibility into the knowledge sources and facts used to inform each prediction. This grants users clearer insight into the model's reasoning process.
RAG models can increase the transparency and factual accuracy of large language models (LLMs) in a few key ways:
- Providing relevant external context - The retrieval module grounds the LLM's generations on real world knowledge, rather than just its training data patterns. This makes the output more factual and credible.
- Citing sources - The specific documents and passages retrieved to augment the prompt are appended to the final response. This allows users to verify the accuracy and origin of the information.
- Reducing hallucination - By conditioning generations on retrieved factual knowledge, RAG models are less likely to fabricate responses or make false claims, a problem for unrestrained LLMs.
- Updating knowledge - The external knowledge sources can be continuously maintained, ensuring LLMs have access to current information rather than becoming outdated over time.
- Explainability - Showing the relevant supplemental documents used provides transparency into the RAG model's reasoning and data sources.
- Computational Efficiency - By avoiding expensive model retraining, RAG's modular architecture reduces the computational and monetary costs of deploying large language model chatbots for businesses.
These external documents are encoded into vector representations using embedding models like BERT. This allows the model to search for semantic similarity between the user's query and the documents. Embeddings can come from multiple sources.
- Use a pretrained language model like BERT or RoBERTa to encode text:
- Input a document into the model to generate an embedding vector representing it. For example, encoding a Wikipedia article about Paris.
- BERT and RoBERTa are trained to produce semantic vector representations of text.
- Fine-tune a language model on in-domain data:
- Further train BERT on a corpus of documents related to the prompts.
- This tailors the embeddings specifically for your retrieval domain. For example, fine-tuning on travel guides and articles to embed vacation-related documents.
- Train a custom autoencoder model:
- Build a sequence-to-vector autoencoder on the document collection.
- The encoder portion learns to produce document embeddings by reconstructing the original text.
- Allows creating embeddings customized for the knowledge domain.
- Use simple aggregation functions like TF-IDF weighted averaging:
- Calculate a word embedding for each token and take a weighted average per document.
- Gives a quick unsupervised approach without training a model.
The optimal embedding approach depends on your model architecture, data, and use case. But techniques like language model encoding and fine-tuning are most common to get high-quality document representations for retrieval.
In all these cases, the ideal solution is to dynamically update the documents or data being searched to make it more relevant. Instead of treating the knowledge source as fixed, new information needs to be continuously integrated.
Some ways to achieve this include:
- Expanding the corpus with recent publications and articles to cover new concepts.
- Switching domains entirely to find better document sources for certain prompts.
- Using APIs or databases that provide real-time, up-to-date information.
- Retraining embeddings on an updated corpus to realign the vector space.
- Iteratively improving the search algorithm and relevance ranking.
By dynamically keeping the knowledge source current and relevant to prompts, the context being retrieved and fed to the language model will improve, enhancing RAG performance.
The most relevant snippets or passages are then retrieved by the module and appended to the original user prompt to provide additional context. This augmented prompt is fed into the foundation language model like GPT-3, which can now leverage the external knowledge to generate more informative, accurate, and grounded responses.
The retrieval module and foundation model work together in an end-to-end fashion. As the user converses with the system, the retrieval keeps updating to find relevant information on-the-fly to augment each new prompt. This allows the system to have access to a vast amount of external knowledge that can make it more intelligent and knowledgeable.
The modular architecture makes RAG models highly scalable - the document collections and retrieval model can be updated independently of the foundation model. This makes it possible to continuously expand the knowledge sources and retrieval capabilities to handle more complex information seeking conversations.
Overall, RAG combines the creative generation of language models with targeted knowledge retrieval. This fusion anchors output in factual context for greater precision and explainability in natural language generation applications.