Pre-training, Fine-tuning & In-context Learning of LLMs 🚀⚡️ Generative AI

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Large Language models have made significant strides in enhancing Natural Language Processing (comprehension and generation). They acquire their capabilities through a multi-stage process encompassing pre-training, fine-tuning, and in-context learning. Explained herewith:

✅ Pre-training of LLMs
➡️ In the initial learning phase, known as pre-training, language models are exposed to vast amounts of unlabeled textual data, including books, articles, and websites.
➡️ The objective here is to capture underlying textual patterns, structures, and semantic knowledge.

✅ Fine-tuning of LLMs
➡️ Fine-tuning, the subsequent step, involves further training a large language model (LLM) on specific tasks or domains. This entails utilizing the LLM as a starting point and training it with labeled data relevant to the particular task or domain. Fine-tuning optimizes model performance by adjusting its weights to better fit the task data.
➡️ Fine-tuning enables models to excel in various specific natural language processing tasks, including sentiment analysis, question answering, machine translation, and text generation.

✅ In-Context Learning of LLMs
➡️ Emerging as an innovative approach, in-context learning combines pre-training and fine-tuning while integrating task-specific instructions or prompts during training. This approach equips models to generate contextually relevant outputs or responses based on provided instructions, leading to enhanced performance on specialized tasks.
➡️ In-context learning has demonstrated promising outcomes across various tasks, including question answering, dialogue systems, text completion, and text summarization.

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Excellent example for explaining pre-training, finetuning and context learning with an LLM. The concept could be understood quickly with your description, thanks

gururajannarasimhan

that is a good analogy to remember this concept, kudos

tahashaikh

poor story telling. you look an java teacher.

aravindr

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