tx · 4Nw53DRLnUS2dA3B28fjzVvoG8zuEXwXwMSU46RQudWV
3NAAoJ554QsZfqE8W8Rg8LsJb79d5b1pDat: -0.00500000 Waves
2025.03.07 17:56 [3533738] invoke 3NAAoJ554QsZfqE8W8Rg8LsJb79d5b1pDat > 3N9tKixzqTYWnEXQxrDQ5pBTGvQd6sFsvmV commitTask()
3N9tKixzqTYWnEXQxrDQ5pBTGvQd6sFsvmV: checked_out_by_Ct2djqZsAz77Ur5Y9pMbgrJR2xn6hDprcsHUYgLfsdkY_chatgpt_8a1TED2MzCs9tyioHcXPrJPW2NCj4AwXQ28MMXqTKTe9_AqqtiUWzxuW2sGQZiUBdYgDuY9J9GaL327FdWiEuh6qc: true -> null
3N9tKixzqTYWnEXQxrDQ5pBTGvQd6sFsvmV: 8a1TED2MzCs9tyioHcXPrJPW2NCj4AwXQ28MMXqTKTe9_AqqtiUWzxuW2sGQZiUBdYgDuY9J9GaL327FdWiEuh6qc_commit_timestamp_chatgpt: 1741359383648
3N9tKixzqTYWnEXQxrDQ5pBTGvQd6sFsvmV: 8a1TED2MzCs9tyioHcXPrJPW2NCj4AwXQ28MMXqTKTe9_AqqtiUWzxuW2sGQZiUBdYgDuY9J9GaL327FdWiEuh6qc_commit_height_chatgpt: 3533738
3N9tKixzqTYWnEXQxrDQ5pBTGvQd6sFsvmV: 8a1TED2MzCs9tyioHcXPrJPW2NCj4AwXQ28MMXqTKTe9_AqqtiUWzxuW2sGQZiUBdYgDuY9J9GaL327FdWiEuh6qc_result_chatgpt: "Large Language Models (LLMs) are a type of artificial intelligence (AI) model that uses deep learning techniques to understand and generate human language. These models are trained on massive amounts of text data to develop the ability to understand and generate written text in a way that is coherent and contextually relevant.
The key characteristic of LLMs is their ability to generate human-like text by predicting the most likely next word in a sequence based on the words that came before it. This is achieved through a process called "auto-regression," where the model is fed a sequence of words and tasked with predicting the next word in the sequence. The model learns to generate text that is coherent and contextually appropriate by analyzing patterns in the training data.
LLMs have gained significant attention in recent years due to their impressive performance on a wide range of natural language processing tasks, such as language translation, text summarization, and question-answering. Some popular examples of LLMs include OpenAI's GPT (Generative Pre-trained Transformer) models and Google's BERT (Bidirectional Encoder Representations from Transformers).
One of the key advantages of LLMs is their ability to adapt to a wide range of language tasks without the need for task-specific training. This makes them highly versatile and cost-effective for a variety of applications in industries such as customer service, content generation, and language translation.
However, LLMs also come with certain limitations and challenges. One of the main concerns is the potential for bias in the training data, which can lead to biased or politically incorrect responses from the model. Additionally, LLMs require significant computational resources and data to train effectively, making them inaccessible to smaller organizations or individuals without access to large-scale computing infrastructure.
In conclusion, Large Language Models represent a cutting-edge advancement in the field of natural language processing, offering powerful capabilities for understanding and generating human language. As research in this area continues to evolve, LLMs are expected to play a central role in shaping the future of AI-driven language applications."
3N9tKixzqTYWnEXQxrDQ5pBTGvQd6sFsvmV: 8a1TED2MzCs9tyioHcXPrJPW2NCj4AwXQ28MMXqTKTe9_AqqtiUWzxuW2sGQZiUBdYgDuY9J9GaL327FdWiEuh6qc_status_chatgpt: "checked_out" -> "done"
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"value": "Large Language Models (LLMs) are a type of artificial intelligence (AI) model that uses deep learning techniques to understand and generate human language. These models are trained on massive amounts of text data to develop the ability to understand and generate written text in a way that is coherent and contextually relevant.\n\nThe key characteristic of LLMs is their ability to generate human-like text by predicting the most likely next word in a sequence based on the words that came before it. This is achieved through a process called "auto-regression," where the model is fed a sequence of words and tasked with predicting the next word in the sequence. The model learns to generate text that is coherent and contextually appropriate by analyzing patterns in the training data.\n\nLLMs have gained significant attention in recent years due to their impressive performance on a wide range of natural language processing tasks, such as language translation, text summarization, and question-answering. Some popular examples of LLMs include OpenAI's GPT (Generative Pre-trained Transformer) models and Google's BERT (Bidirectional Encoder Representations from Transformers).\n\nOne of the key advantages of LLMs is their ability to adapt to a wide range of language tasks without the need for task-specific training. This makes them highly versatile and cost-effective for a variety of applications in industries such as customer service, content generation, and language translation.\n\nHowever, LLMs also come with certain limitations and challenges. One of the main concerns is the potential for bias in the training data, which can lead to biased or politically incorrect responses from the model. Additionally, LLMs require significant computational resources and data to train effectively, making them inaccessible to smaller organizations or individuals without access to large-scale computing infrastructure.\n\nIn conclusion, Large Language Models represent a cutting-edge advancement in the field of natural language processing, offering powerful capabilities for understanding and generating human language. As research in this area continues to evolve, LLMs are expected to play a central role in shaping the future of AI-driven language applications."
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