Summarize the key points of the following passage about the impact of large language models on summarization:

The advent of large language models, such as GPT-3 and BART, has had a significant impact on the field of summarization. These models, pre-trained on vast amounts of text data using self-supervised learning techniques, have demonstrated remarkable capabilities in understanding and generating natural language. When fine-tuned on summarization datasets, these models can produce high-quality abstractive summaries that capture the key points of the input text while rephrasing and condensing the information in a coherent and fluent manner. Moreover, the general knowledge and reasoning abilities acquired during pre-training enable these models to handle diverse topics and domains, potentially reducing the need for extensive task-specific training data. However, these models are also computationally intensive, requiring significant resources for training and inference, which can limit their accessibility and scalability. Additionally, issues such as factual inconsistencies, hallucinations, and biases inherited from the training data remain challenges that need to be addressed.
Summarize the key points of the following passage about the impact of large language models on summarization:

The advent of large language models, such as GPT-3 and BART, has had a significant impact on the field of summarization. These models, pre-trained on vast amounts of text data using self-supervised learning techniques, have demonstrated remarkable capabilities in understanding and generating natural language. When fine-tuned on summarization datasets, these models can produce high-quality abstractive summaries that capture the key points of the input text while rephrasing and condensing the information in a coherent and fluent manner. Moreover, the general knowledge and reasoning abilities acquired during pre-training enable these models to handle diverse topics and domains, potentially reducing the need for extensive task-specific training data. However, these models are also computationally intensive, requiring significant resources for training and inference, which can limit their accessibility and scalability. Additionally, issues such as factual inconsistencies, hallucinations, and biases inherited from the training data remain challenges that need to be addressed.
user
Large language models like GPT-3 and BART, pre-trained on vast amounts of text data using self-supervised learning, have had a significant impact on summarization. When fine-tuned on summarization datasets, these models can produce high-quality abstractive summaries that capture key points while rephrasing and condensing information coherently and fluently. Their general knowledge and reasoning abilities enable them to handle diverse topics and domains, potentially reducing the need for extensive task-specific training data. However, these models are computationally intensive, requiring significant resources, which can limit their accessibility and scalability. Issues such as factual inconsistencies, hallucinations, and biases inherited from training data remain challenges to be addressed.
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: Can you sum up what these former survivor players said about meeting Jeff Probst in a few sentences?

"Survivor: Island of the Idols" player Elaine Stott said that although she was nervous before she met the "Survivor" host, the fact that she was sitting in front of producers and Probst "didn't really matter to me." 

"I told dirty jokes, I flirted, I cussed, I made fun of myself. That's just how I am in general," Stott told Insider. "... I said, 'Jeff, you're just a man, you put your britches on just like I do.'" 

Freberg added that the interviewers just "tell you to start talking" instead of asking direct questions, and Probst sits there "trying to size you up and trying to be intimidating."

"You just need something funny to say, so I made it up on the spot," Freberg recalled. "... I look at Jeff and I go, 'My grandma's gonna freak the f--- out when she finds out I met you.'"

Freberg added that his joke "got a big laugh," so he was "off to a good start after that."
Response: When Elaine Stott was in front of Probst and producers her nervousness went away. Freberg said the producers didn't ask questions and he was told to just start talking. Jeff sized them up, but Freeberg came up with a funny joke from the start and that put them on good terms for the rest of the interview.

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.
