How to customize LLMs for topics they weren't trained for
New study provides insights on the effectiveness of LLM RAG and fine-tuning for topics that are not included in the model's training data.
A new study by researchers at Radboud University and the University of Amsterdam explores the effect of retrieval-augmented generation (RAG) and fine-tuning (FT) on LLM applications when your data is not present in the model’s training examples.
The study explores the effects of RAG, FT, and RAG+FT on LLMs.
The researchers use different RAG techniques, different fine-tuning techniques (full FT vs parameter-efficient fine-tuning), and different data generation techniques (end-to-end training vs prompt generation).
Based on their findings, we provide a recipe for configuring LLMs for specialized applications in a way to get the best results at the lowest costs.
Read the full article on TechTalks.
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