Notes:

Introduction

• General purpose seq2seq models are great, and are getting really powerful. These models, like GPT-3 and T5, can be fine-tuned for almost any NLP task, such as translation, classification, and text generation.
• They capture a lot of knowledge within their parameters due to their large-scale pre-training and have strong results on my many tasks and are almost applicable to anything.
• But currently they hallucinate, cannot access data/ context, and are difficult to update.
• We also know that external retrieval of knowledge is great and help more accurate results, we have dense retrieval.
• But a problem with this is we need supervision or heuristic based retrieval and need some way to integrate into downstream tasks.

<aside> 💡 The main question is how can we combine the strengths of the explicit knowledge retrieval and seq2seq?

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• The goal of this paper is to combine the extensive knowledge stored in seq2seq models with the precise information from retrieval systems.
• This integration aims to produce more accurate and contextually appropriate responses in various NLP tasks.

The RAG

The architecture

• RAG models combine a retriever and a generator to leverage external knowledge, where the retriever finds relevant documents based on the input query and the generator uses these documents to produce detailed and accurate responses.
• The Retriever Model typically uses models like BERT to encode the input query and search for the most relevant documents, providing context and knowledge for the generation phase. The Generator Model, often based on transformers like BART, GPT-3, or T5, takes the original query and retrieved documents to generate a coherent and contextually appropriate response.
• The user inputs a query, which is processed by the retriever model to search a large-scale corpus and find multiple relevant documents. These documents, along with the original query, are then fed into the generator model, which synthesizes the information to produce a detailed and accurate response.
• The RAG-Sequence Model first retrieves documents and then generates the entire output sequence based on each document, combining the generated sequences to produce the final output. The RAG-Token Model retrieves documents and generates each token by dynamically considering the influence of different documents at every step.
• The pre-trained generator model uses models like BART, GPT-2, or T5 to generate the response based on the input query and retrieved documents. The pre-trained retriever model uses dense passage retrievers, such as DPR from Facebook AI Research, to find relevant documents from an indexed knowledge base like Wikipedia.
• RAG models are trained by minimizing the negative log-likelihood of generating the correct output sequence given the input sequence, using pairs of input and output (e.g., questions and answers). Fine-tuning both the retriever and generator on specific tasks improves performance by adjusting the query encoder to better retrieve relevant documents.

The models

• Training RAG Models: RAG models are trained using input sequences to retrieve relevant documents and use these documents as context when generating responses. The goal is to minimize errors in generating the correct output sequence.
• Dense Passage Retriever (DPR): The retriever uses models like BERT to encode the input query and documents into vectors. These vectors help in finding the most relevant documents using a method called maximum inner product search (MIPS).
• RAG-Token Decoding: In the RAG-Token model, each word or token in the response is generated by looking at the influence of different retrieved documents. It uses a method called beam search to ensure the best possible response.
• RAG-Sequence Decoding: In the RAG-Sequence model, the entire response sequence is generated for each retrieved document. The model then combines these sequences to produce the final response using a method called "Fast Decoding."
• Document Indexing: A large collection of documents, like Wikipedia, is indexed for fast retrieval. This indexed knowledge base helps the retriever quickly find relevant documents during the response generation process.
• Model Optimization: The models are optimized using backpropagation with Adam. This process refines both the retriever and the generator, making them better at finding relevant documents and generating accurate responses.

My annotations

Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.pdf

RAG Architecture

<aside> 💡 This diagram illustrates the process of Retrieval-Augmented Generation (RAG). The query encoder converts the input query into a vector. The retriever uses this vector to search a document index and retrieves the most relevant documents. These documents, along with the query, are then passed to the generator, which produces a final, contextually appropriate response by combining the information from the retrieved documents.

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<aside> 💡 Beam Search and Decoding: Beam search is a strategy used during decoding to generate multiple possible responses and select the best one. This ensures the final response is high quality. The RAG-Token model uses beam search at each step to dynamically consider different documents' influences. The RAG-Sequence model uses beam search to generate responses for each retrieved document, and "Fast Decoding" combines these responses into a final, coherent answer.

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Performance Results and final thoughts

Performance

RAG models are tested on various knowledge-intensive tasks to evaluate their performance. They excel in tasks where accurate and contextually rich responses are crucial. By combining retrieval and generation, RAG models significantly improve performance compared to traditional seq2seq models.

Results

1. Open-Domain Question Answering: In open-domain question answering (QA), RAG models retrieve relevant documents and generate precise answers. They outperform models that rely solely on parametric knowledge, demonstrating their strength in integrating retrieved information. The ability to combine the best of both retrieval and generation enables RAG models to handle complex questions effectively.
2. Abstract Question Answering: For tasks requiring more elaborate answers, like abstract QA, RAG models generate full sentences instead of short answers. This capability shows the flexibility and depth of RAG models in handling different types of queries, making them suitable for diverse applications beyond simple QA.
3. Jeopardy Question Generation: RAG models are also evaluated on generating Jeopardy-style questions from given answers. This task tests their ability to produce detailed, knowledge-rich questions based on facts. RAG models perform well, generating accurate and specific questions that demonstrate their understanding and retrieval capabilities.
4. Fact Verification: In fact verification tasks, RAG models assess the truthfulness of claims by retrieving supporting or refuting documents. They classify claims as true, false, or not enough information based on the retrieved evidence. This showcases their potential in applications requiring high accuracy and reliability, such as misinformation detection.
5. Training and Fine-Tuning: The joint training of retriever and generator components allows RAG models to learn effectively from input-output pairs. Fine-tuning on specific datasets further enhances their performance, making them highly adaptable to various knowledge-intensive tasks. This training approach ensures that both retrieval and generation are optimized for the best results.

Final Thoughts

RAG represents a significant advancement in NLP, combining the strengths of retrieval and generation. RAG models leverage extensive external knowledge and the capabilities of powerful generation models to produce accurate, contextually rich responses. Their performance across diverse tasks highlights their versatility and potential for wide-ranging applications in NLP.

Q&A

Question: Is there a danger that the generator could still hallucinate with the information acquired if it was not retrieved in the retriever step?

• Answer: Yes, there is a possibility of hallucination. The generator could still make up facts if the retrieved documents are not relevant. However, this is mitigated by the end-to-end training process where the generator learns to rely more on the retrieved documents.

Question: Can RAG models use non-textual knowledge bases like knowledge graphs?

• Answer: Yes, in theory, RAG models can be extended to use non-textual knowledge bases such as knowledge graphs. This would involve adapting the retrieval and integration processes to handle structured data.

Question: How does the system handle questions that it cannot answer or shouldn't answer?

• Answer: RAG models can struggle with unanswerable questions. One approach is to have a mechanism that recognizes when a question cannot be answered based on the retrieved documents and then either refuses to answer or provides a response indicating the lack of information.

Question: How easy is it to update the knowledge base in RAG models as new information becomes available?

• Answer: Updating the knowledge base in RAG models is relatively straightforward. New documents can be added to the index, and the retriever can be fine-tuned to improve retrieval of up-to-date information without needing to retrain the entire model.

Question: How does the encoding of queries and documents affect the retrieval and generation process?

• Answer: The encoding process is crucial as it transforms queries and documents into vectors that the retriever and generator can use. High-quality encoding ensures better retrieval of relevant documents and more accurate generation of responses.