Summarize the following information about text generation with language models in a few sentences: Large language models are neural networks trained on vast amounts of text data to learn patterns and relationships in natural language. These models can then generate new, coherent text by predicting the most likely next word or sequence, based on the preceding context. Popular examples include GPT-3, BERT, and T5, which demonstrate impressive capabilities in tasks like open-ended text generation, question answering, and text summarization. However, language models can also exhibit biases, inconsistencies, and factual errors due to the opaque nature of their training. Techniques like prompting, fine-tuning on domain-specific data, and including external knowledge sources aim to control and improve the quality of generated text.
Summarize the following information about text generation with language models in a few sentences: Large language models are neural networks trained on vast amounts of text data to learn patterns and relationships in natural language. These models can then generate new, coherent text by predicting the most likely next word or sequence, based on the preceding context. Popular examples include GPT-3, BERT, and T5, which demonstrate impressive capabilities in tasks like open-ended text generation, question answering, and text summarization. However, language models can also exhibit biases, inconsistencies, and factual errors due to the opaque nature of their training. Techniques like prompting, fine-tuning on domain-specific data, and including external knowledge sources aim to control and improve the quality of generated text.
user
Large language models like GPT-3, BERT, and T5 leverage neural networks trained on massive text data to generate coherent text by predicting likely word sequences based on context. While powerful for open-ended generation, question answering, and summarization, these models can exhibit biases, inconsistencies, and factual errors. Approaches like prompting, fine-tuning, and integrating external knowledge sources aim to improve the quality and control of generated text.
assistant
Generate a series of (instruction, response) pairs that are similar in context and structure to the example provided below. Each pair should consist of a concise instruction followed by an appropriate, detailed response. The instruction should pose a clear task or question, while the response should provide a comprehensive answer or solution that could be understood by someone with a basic understanding of the subject.

Example pair: 
Instruction: Condense the following piece of history into one or two sentences for me:

Before 1895, the area corresponding to modern-day South Gyeongsang Province was part of Gyeongsang Province, one of the Eight Provinces of Korea during the Joseon dynastic kingdom. In 1895, southern Gyeongsang was replaced by the districts of Jinju in the west and Dongnae (modern-day Busan) in the east. In 1896, they were merged to form South Gyeongsang Province.

The provincial capital was originally at Jinju; it moved in 1925 to Busan.During the Japanese rule of Korea, the province was known as Keishōnan-dō. In 1948, South Gyeongsang Province became part of South Korea. In 1963, Busan separated from South Gyeongsang Province to become a Directly Governed City (Jikhalsi). In 1983, the provincial capital moved from Busan to Changwon.
Response: Modern-day South Gyeongsang Province used to be part of Gyeongsang Province until 1895, with its current provincial capital being Changwon after changing twice. 

Your task is to generate more pairs that maintain this level of clarity and detail. The topic is Summarization. Write a long text of instruction by yourself, then summarize the given instruction in a response. Ensure that the responses are informative and accurate, suitable for an educational context. 

Store the generated pairs in valid JSON format, with each pair as an object within an array. Each object should have two key-value pairs: "instruction" and "response". For instance: 

{"contents": [{"instruction": "text", "response": "text"}, {"instruction": "text", "response": "text"}, ...]} 

Remember to maintain consistency in the format and ensure the generated pairs are diverse and cover a broad range of subjects. You must return the response in the asked format and you must not add any additional text in your response.
