Large Language Models and Their New Technique
Large Language Models (LLMs) have become an integral part of how people interact with machines through transforming Natural Language Processing (NLP). These models carry out various functions like question answering, text summarization, and code completion. However, their ability to function in programming, mathematics, biomedical sciences, and finance is limited.
To improve LLMs, a new study suggests a post-pretraining technique called block expansion. This approach can extend Transformer blocks without causing catastrophic forgetting, allowing the model to use domain-specific information without diminishing its overall capabilities.
The researchers have then developed a new model called LLAMA PRO from the technique, which shows remarkable performance on general tasks and domain-specific tasks such as programming and mathematics. The model is flexible, reducing the risk of catastrophic forgetting and excelling in handling different applications.
Through this study, the researchers have demonstrated that LLMs can become more adaptable and powerful language agents, capable of performing well in various settings and tasks.
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