In the first approach, RAG-Sequence, the model uses the same document to generate the complete sequence. The first step is to collect demonstration data/labels. The next step is to define evaluation metrics. Honestly, I don’t really believe that any of these eval metrics capture what we care about. Finally, sometimes the best eval is human eval aka vibe check. To address these downsides, they introduced RAG (aka semi-parametric models). RAG has its roots in open-domain Q&A. These could be for straightforward tasks such as document classification, entity extraction, or summarization, or they could be more complex such as Q&A or dialogue. We’re much more interested in a nice relaxing cuppa. A short distance from the main land, you won’t find a much nicer location! But if your fine-tuning is more intensive, such as continued pre-training on new domain knowledge, you may find full fine-tuning necessary. We may also need to update the model architecture, such as when the pre-trained model’s architecture doesn’t align with the task. Then, select a pre-trained model. Retrieval Augmented Generation (RAG), from which this pattern gets its name, highlighted the downsides of pre-trained LLMs. In short, RAG applies mature and simpler ideas from the field of information retrieval to support LLM generation.
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Caching is a technique to store data that has been previously retrieved or computed. Query larger open models with permissive licenses: With prompt engineering, we might be able to elicit reasonable demonstration data from a larger model (Falcon 40B Instruct) that can be used to fine-tune a smaller model. An early Meta paper showed that retrieving relevant documents via TF-IDF and providing them as context to a language model (BERT) improved performance on an open-domain QA task. These include not being able to expand or revise memory, not providing insights into generated output, and hallucinations. But by the 1996, manic depression and paranoia gripped the actress in a well-publicized nervous breakdown in which she cut her hair to avoid being recognized and went missing for days before being found hiding in a suburban California backyard. And if we’re using LoRA, we might want to tune the rank parameter (though the QLoRA paper found that different rank and alpha led to similar results). We’ve dug deep to dish the finger-waving, “oh no you didn’t” interstate drama, so you will definitely want to share this U.S.