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LLM Refinement

Definition

LLM refinement involves improving the performance and outputs of large language models for specific applications or data sets. This process encompasses various techniques, such as fine-tuning, prompt engineering, or applying additional training data, to enhance an LLM’s accuracy, relevance, and coherence in a particular domain. The goal is to optimize the model’s ability to understand specialized terminology and generate pertinent responses for specific tasks. This leads to more precise and valuable insights from complex information, such as cryptocurrency reports.