The article discusses Self-RAG, a method that improves upon the standard Retrieval Augmented Generation (RAG) architecture. Self-RAG uses fine-tuned language models to determine the relevance of a context and generates special tokens accordingly. It outperforms other models in various tasks and does not change the underlying language model. However, there is room for improvement in dealing with fixed context lengths. The code for inference using Self-RAG is provided in the GitHub repository.
How Self-RAG Could Revolutionize Industrial LLMs
Large language models (LLMs) have the potential to revolutionize industries like finance by quickly analyzing vast amounts of data. However, traditional retrieval augmented generation (RAG) models have limitations, such as incomplete answers and irrelevant contexts. This is where Self-RAG comes in.
What is Self-RAG?
Self-RAG is a clever solution that enhances LLMs by appending special tokens to generated text. These tokens help determine the relevance of the context, whether the generated text is supported or not, and the utility of the generation.
How is Self-RAG Trained?
Self-RAG is trained in a two-step process. First, a simple LM is trained to classify generated outputs and append the relevant special token. Then, the generator model learns to generate continuations and special tokens for retrieval and critique.
Evaluating Self-RAG
Self-RAG has been evaluated against various tasks, including fact verification, multiple-choice reasoning, and Q&A. It performs well and outperforms other similar models in many cases.
Inference with Self-RAG
For inference, the self-RAG repository suggests using the vllm library for LLM inference. You can use the provided code examples to query the model and get predictions.
Advantages of Self-RAG
Self-RAG offers several advantages over vanilla LLMs:
- Adaptive Passage Retrieval: Self-RAG can retrieve relevant context until all the necessary information is found.
- More Relevant Retrieval: The special tokens help improve the retrieval of relevant context.
- Outperforms Similar Models: Self-RAG performs better than other similar models and even beats ChatGPT in some tasks.
- No Change to Underlying LM: Self-RAG adds special tokens without altering the underlying text generation process, avoiding biases.
Room for Improvement
While Self-RAG is a powerful tool, there is room for improvement. Adding a summarization component could help with fixed context lengths. OpenAI’s GPT-4 128k context window update also shows promise for increasing context length. Future improvements in RAG-specific tuning of language models are expected.
For more information and the inference code, visit the Self-RAG GitHub repository.
How Self-RAG Could Revolutionize Industrial LLMs
If you want to evolve your company with AI and stay competitive, consider how Self-RAG could revolutionize your industrial LLMs. AI can redefine your way of work and provide real business impacts. Here are some steps to get started:
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- Implement Gradually: Start with a pilot, gather data, and expand AI usage judiciously.
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