Sure, here is how I would explain Retrieval Augmented Generation (RAG) to an Economics grad student:
Think of RAG as a technique to generate coherent paragraphs or long-form text by combining a language model with a knowledge retrieval system. The language model, like GPT-3, is trained on a large text corpus to predict the next word given the previous words. It learns patterns in language and can generate pretty fluent text. However, it doesn't have any true knowledge or memory of facts.
A retrieval system can store a large collection of facts, ideas, or paragraphs as 'memories'. The RAG model first retrieves one or more relevant memories based on your prompt, then uses those memories as additional context for the language model to generate from. This helps the language model stay grounded and generate more coherent information that is shaped by the retrieved memories.
So in Economics terms, you can think of the language model as a theoretical framework or model that can generate hypotheses and ideas. But without data or empirical evidence, those hypotheses aren't very well-informed. The memories in the retrieval system are like economic data, statistics, or case studies. By retrieving relevant empirical examples first before generating text, the RAG model produces predictions, analyses or essays that are more strongly linked to the evidence, like an Economist who generates theories and insights grounded in data and cases, not just abstract theory alone.
Does this make sense? Let me know if you have any other questions! I'm happy to explain further or provide some examples to illustrate the RAG technique.
Yes, that's a good way to think about it. A RAG model does effectively increase the context that a language model can utilize when generating text.
In a typical language model, the context is just the previous words or tokens that it has generated so far. The model has no explicit long-term memory of other contexts it has seen before. This limits how much coherence and factual accuracy it can maintain, especially over long sequences.
By retrieving external memories in addition to its own generated context, a RAG model gives the language model additional context to condition on. This longer effective context helps the model stay more coherent and grounded. It understands the general topic or themes not just based on the few words it has generated so far, but based on the retrieved memories which can span many sentences or paragraphs.
So you can say that RAG helps extend the model's context length and memory. The retrieved passages provide more context for the language model to link back to, remember facts from, and stay consistent with. This allows RAG models to generate longer, more complex outputs that have stronger discourse structure - like generating multiple paragraphs on a topic rather than just a few sentences.
The exact context length will depend on how much information is retrieved and fed into the model. More memories mean a longer effective context, but the model also has to handle more information. RAG allows flexible context lengths tailored for different generation tasks. But in general, yes RAG enables language models to utilize longer context which improves their coherence and factual consistency.
Does this make sense? Let me know if you have any other questions!
Good question. The RAG model needs to retrieve memories that are relevant for the generation task, not just any random memories. It does this through two mechanisms:
1. Memory indexing: The retrieved memories are indexed in a latent space, where similar memories are close together and dissimilar memories are far apart. The RAG model first encodes the prompt (e.g. a question) into the same latent space. It can then retrieve memories that are nearby in the latent space, meaning they are relevant to the prompt.
2. Cross-attention: The RAG model uses an attention mechanism to compare the prompt encoding to each candidate memory. Memories that have a higher attention weight, meaning they are more aligned and relevant to the prompt, are more likely to be retrieved. This helps filter out irrelevant or dissimilar memories.
For example, if the prompt is a question about economic theory, the RAG model will retrieve memories discussing or explaining economic concepts, not memories about biology or movies. The latent space and attention weights will guide it to select memories relevant to economics.
Some additional techniques that help improve relevance include:
- Diversity promotion: Adding a penalty term to encourage retrieving diverse, non-redundant memories. This avoids retrieving multiple very similar memories.
- Hard negative mining: During training, the model is fed some irrelevant memories for the prompt in addition to relevant ones. It learns to avoid assigning high attention weights to these "hard negative" examples.
- Reinforcement learning: Feedback can be given to the model specifying which retrieved memories were most relevant or irrelevant for a prompt. The model incorporates this feedback to improve its memory selection over time.
So in summary, the RAG model relies on 1) memory indexing to organize memories in a latent space, 2) cross-attention over the prompt encoding and memory candidates to assess relevance, and 3) additional techniques like hard negative mining and reinforcement learning to further improve its memory discrimination abilities.
Does this help explain how RAG models retrieve relevant external memories? Let me know if you have any other questions!
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