Human : I want you act like a prompt engineer who is working on building a chatbot for research papers. Your goal is to write prompt for gpt and let it generated brainstorm questions based on current user conversations and current paper
Chatgpt : Sure, I can help you with that! Here's a prompt that you can use to generate brainstorm questions based on the user's conversation and the current research paper:
Prompt: Given the current conversation and the research paper being discussed, generate brainstorm questions that can help deepen the user's understanding of the paper. Your questions should be clear, concise, and relevant to the paper's main topic.
Example conversation:
User: I'm interested in learning more about the use of neural networks in natural language processing.
Bot: Sure, I can help with that! I have a research paper here that explores the use of neural networks for sentiment analysis in social media data. Would you like to discuss that?
Example paper:
Title: A Deep Learning Approach to Sentiment Analysis in Social Media Data
Authors: John Smith, Jane Doe, and Sarah Lee
Abstract: In this paper, we present a deep learning approach to sentiment analysis in social media data. Specifically, we use a convolutional neural network (CNN) to classify tweets as positive, negative, or neutral based on their content. Our model achieves state-of-the-art performance on several benchmark datasets, demonstrating the effectiveness of the CNN architecture for this task.
Generated questions:
How does the use of a convolutional neural network improve sentiment analysis in social media data?
What benchmark datasets were used to evaluate the performance of the model?
Can this approach be applied to other types of social media data besides Twitter?
How does this approach compare to other sentiment analysis techniques, such as rule-based or machine learning-based methods?
What are the implications of this research for businesses or organizations that use social media for customer engagement?
What are the limitations of using a CNN for sentiment analysis, and how can they be addressed?
Are there any potential ethical concerns related to using this approach to analyze social media data?
daodao97
commented
1 year ago
有很多其他chatpaper类应用，都实现了自动头脑风暴的功能。我问了一下chatgpt，头脑风暴的prompt该怎么做：https://poe.com/s/bxwSkBQdat9OZCJSBsv0 可以参考一下：
Human : I want you act like a prompt engineer who is working on building a chatbot for research papers. Your goal is to write prompt for gpt and let it generated brainstorm questions based on current user conversations and current paper
Chatgpt : Sure, I can help you with that! Here's a prompt that you can use to generate brainstorm questions based on the user's conversation and the current research paper:
Prompt: Given the current conversation and the research paper being discussed, generate brainstorm questions that can help deepen the user's understanding of the paper. Your questions should be clear, concise, and relevant to the paper's main topic.
Example conversation: User: I'm interested in learning more about the use of neural networks in natural language processing. Bot: Sure, I can help with that! I have a research paper here that explores the use of neural networks for sentiment analysis in social media data. Would you like to discuss that?
Example paper: Title: A Deep Learning Approach to Sentiment Analysis in Social Media Data Authors: John Smith, Jane Doe, and Sarah Lee Abstract: In this paper, we present a deep learning approach to sentiment analysis in social media data. Specifically, we use a convolutional neural network (CNN) to classify tweets as positive, negative, or neutral based on their content. Our model achieves state-of-the-art performance on several benchmark datasets, demonstrating the effectiveness of the CNN architecture for this task.
Generated questions:
How does the use of a convolutional neural network improve sentiment analysis in social media data? What benchmark datasets were used to evaluate the performance of the model? Can this approach be applied to other types of social media data besides Twitter? How does this approach compare to other sentiment analysis techniques, such as rule-based or machine learning-based methods? What are the implications of this research for businesses or organizations that use social media for customer engagement? What are the limitations of using a CNN for sentiment analysis, and how can they be addressed? Are there any potential ethical concerns related to using this approach to analyze social media data?