我们聊聊如何增强ChatGPT处理模糊问题能力

1. In 2023, the LLM use cases can be divided into two categories: generation AI and decision-making. Decision-making scenarios are expected to have higher business value.
2. The generation AI scenario mainly includes dialogue interaction, code development, intelligent agents, etc.
3. NLP applications include text classification, machine translation, sentiment analysis, automatic summarization, etc.

The application scenarios of the large model industry can be divided into generation and decision-making scenarios.

Q1: What are the use cases of the large language model in 2023?
A1: The use cases of large language models can be divided into generation AI and decision-making.
Q2: What are their differences, and can you provide examples?

1. BERT and GPT are important models in the field of NLP, but they have significant differences in their design and application scenarios.
2. The difference between large models and small models lies in their scale and complexity. Large models usually have more parameters and more complex structures, requiring more computational resources and time for training and inference. Small models, on the other hand, are relatively simple with fewer parameters and faster training and inference speeds.
3. However, there is no additional information to distinguish between these two products because they appear very similar.

prompt = f'''Please generate a new question adhering to the following guidelines:
1. Complete any missing pronouns or conditions in the question based on the context.
2. Retain the original question if it's already complete.




{history}
Question: {question}'''

history = ''' What is NLP?
NLP stands for Natural Language Processing. It is a field of computer science and artificial intelligence that focuses on the interaction between computers and humans using natural language. '''
question = 'What problem does it solve?'

What problem does Natural Language Processing (NLP) solve?

history = '''When was GPT4 released?
GPT4 was released in 2023'''
question = 'What progress has been made in computer vision this year?'

What progress has been made in computer vision this year?

history = '''What is GPT3?
GPT-3 (Generative Pre-trained Transformer 3) is a state-of-the-art language processing model developed by OpenAI.
When was GPT4 released?
GPT4 was released in 2023'''
question = 'What is the difference between them? What are the advantages of the latter?'

Sorry, the information provided about GPT-4's release date is not accurate as it has not been officially announced by OpenAI. Therefore, it is not possible to answer the question about the difference between GPT-3 and GPT-4 or the advantages of the latter.

REWRITE_TEMP = f'''
HISTORY:
[]
NOW QUESTION: Hello, how are you?
NEED COREFERENCE RESOLUTION: No => THOUGHT: Consequently, the output question mirrors the current query. => OUTPUT QUESTION: Hello, how are you?
-------------------
HISTORY:
[Q: Is Milvus a vector database?
A: Yes, Milvus is a vector database.]
NOW QUESTION: How to use it?
NEED COREFERENCE RESOLUTION: Yes => THOUGHT: I must substitute 'it' with 'Milvus' in the current question. => OUTPUT QUESTION: How to use Milvus?
-------------------
HISTORY:
[]
NOW QUESTION: What are its features?
NEED COREFERENCE RESOLUTION: Yes => THOUGHT: Although 'it' requires substitution, there's no suitable reference in the history. Thus, the output question remains unchanged. => OUTPUT QUESTION: What are its features?
-------------------
HISTORY:
[Q: What is PyTorch?
A: PyTorch is an open-source machine learning library for Python. It provides a flexible and efficient framework for building and training deep neural networks.
Q: What is Tensorflow?
A: TensorFlow is an open-source machine learning framework. It provides a comprehensive set of tools, libraries, and resources for building and deploying machine learning models.]
NOW QUESTION: What is the difference between them?
NEED COREFERENCE RESOLUTION: Yes => THOUGHT: 'Them' should be replaced with 'PyTorch and Tensorflow' in the current question. => OUTPUT QUESTION: What is the difference between PyTorch and Tensorflow?
-------------------
HISTORY:
[{history}]
NOW QUESTION: {question}
NEED COREFERENCE RESOLUTION: '''

history = '''When was GPT-4 released?
GPT-4 was released in 2023'''
question = 'What progress has been made in computer vision this year?'

Yes => THOUGHT: I need to replace "this year" with "2023" in the now question. => OUTPUT QUESTION: What progress has been made in computer vision in 2023?

history = '''What is GPT-3?
GPT-3 (Generative Pre-trained Transformer 3) is a state-of-the-art language processing model developed by OpenAI.
When was GPT-4 released?
GPT-4 was released in 2023'''
question = 'What is the difference between the two? What are the advantages of the latter?'

Yes => THOUGHT: I need to replace 'the two' with 'GPT-3 and GPT-4' and 'the latter' with 'GPT-4' in the now question. => OUTPUT QUESTION: What is the difference between GPT-3 and GPT-4? What are the advantages of GPT-4?