Problem with Pre-trained LLMs:
- Hallucinations: LLMs can generate plausible-sounding but incorrect or nonsensical answers. This is often due to the fact that these models generate text based on patterns learned during training rather than verifying facts.
- Limited Scope of Training Corpus: LLMs might not have encountered certain information during training, especially if the information is domain-specific or has been updated after the model's training period.
- Lack of Access to Latest Information: Since LLMs are static once trained, they do not have real-time access to new information or updates. This can lead to answers that are outdated or irrelevant in the current context.
- Question: The input question posed by the user.
- Retriever: A component that searches for and retrieves the most relevant external documents that may contain information necessary to answer the user's question.
- External Knowledge: The set of documents retrieved by the retriever, which are considered relevant to the question.
- Generator (LLM): The language model that uses the information from the retrieved documents to generate an answer.
- User's Question: The process begins with a question from the user.
- Retrieving Relevant Documents: The retriever component analyzes the user's question and searches a database or corpus for documents that are likely to contain relevant information. This is typically done using techniques like: Vector Search: Representing documents and queries as vectors and finding the closest matches. Keyword Matching: Using keywords from the question to find matching documents.
External Knowledge: The retriever compiles a set of top-k documents that are deemed most relevant. These documents form the external knowledge base.
- Generating the Answer: The generator (LLM) takes these retrieved documents as context and generates an answer. This allows the LLM to produce answers that are grounded in up-to-date and specific information from the external documents.
- Noise Robustness: The model's ability to handle and filter out irrelevant or noisy information within the retrieved documents. It ensures that the generator uses only the most pertinent information.
- Negative Rejection: The ability of the model to recognize when it does not have sufficient information to answer a question accurately and therefore refrain from providing a misleading or incorrect answer.
- Information Integration: The capacity to synthesize information from multiple sources and create a coherent and comprehensive answer, particularly useful for complex questions that require diverse pieces of information.
- Counterfactual Robustness: The ability to detect and handle known errors or contradictions within the retrieved documents, ensuring that such misinformation does not influence the generated answer.
- Context Relevance: The retrieved context must be directly relevant to the user's question. This ensures that the documents used to generate the answer are applicable to the query.
- Answer Relevance: The generated answer must address the user's question directly and appropriately. It should not deviate from the topic or provide extraneous information.
- Faithfulness: The generated answer must remain faithful to the information contained in the retrieved documents. It should accurately reflect the content without introducing distortions or inaccuracies.
- Effective Retrieval: The retriever must be proficient at finding the most relevant documents that contain the necessary information to answer the question.
- Generation Utilization: The generator must be capable of effectively using the retrieved documents to produce a coherent, accurate, and relevant answer.
The Basic RAG model enhances the capabilities of LLMs by addressing their limitations with hallucinations, outdated information, and lack of real-time data access. By integrating a retrieval step, the model ensures that answers are grounded in the most relevant and current information available. This approach significantly improves the accuracy, relevance, and faithfulness of the generated responses, making it a powerful tool for various question-answering tasks.
