/openai-case

Primary LanguagePython

openai-study

Large language models

Large language models (LLMs) are deep learning algorithms that can recognize, summarize, translate, predict, and generate content using very large datasets. LLMs largely represent a class of deep learning architectures called transformer networks. A transformer model is a neural network that learns context and meaning by tracking relationships in sequential data, like the words in this sentence.

LLM are stateless, so far a conversation, the chat is put in memory, providing the full conversation to IA.

Prompting Principles

  • Principle 1: Write clear and specific instructions
  • Principle 2: Give the model time to “think”

Principle 1

Tactic 1: Use delimiters to clearly indicate distinct parts of the input

  • Delimiters can be anything like: ```, """, ---, < >, <tag> </tag>, `:`
  • case-delimeters.py

Tactic 2: As for structured data like JSON or HTML

  • case-list.py

Tactic 3: Check whether conditions are satisfied

Check assumptions required to do the task

  • case-list.py

Principle 2

  • case-give-time-to-think.py

Tactic 1: Specify the steps required to complete a task

Tactic 2: Instruct the model to work out its own solution before rushing to a conclusion

Model Limitations: Hallucinations

  • Make statements tha sound plausible

Reducing Hallucinations

  • First find relevant information, then answer the question based on the relevant infomration

Iterative Prompt

Idea -> Implementation (code/data) & Prompt -> Experimental result -> Error Analysis

<- <- <- <- <- <- <- <- <- <- <- <- <- <- <- <- <- <- <- <- <- <- <- <- <- <- <- <-

Capabilities

Summarizing

  • case-summarizing.py

Inferring

  • Can analysis:
    • Sentiment (positive/negative)
    • Types of emotions found in a text
         prompt = f"""
     Is the writer of the following review expressing anger?\
     The review is delimited with triple backticks. \
     Give your answer as either yes or no.
    • Extract topics from a text
        prompt = f"""
     Determine five topics that are being discussed in the \
     following text, which is delimited by triple backticks.

     Make each item one or two words long. 

     Format your response as a list of items separated by commas.

     Text sample: '''{story}'''
     """

Transforming

Large language models are very good at transforming its input to a different format, like translating to antoher lanaguage, grammar checking, format parsing (JSON, HTML, etc)

Expanding

Generating a extensive and qualitative text from a prompt.

  • case-expanding.py

LangChain

LangChain is a framework for developing applications powered by language models.

  • Pyhton and typescript/JS
  • Open-source
  • Focused on composition and modularity

Good to reuse prompt templates, easiest to extract keywords or topics from the prompts and parse outputs.

  • Memory Buffer: Stores the conversation to reuse on chat/prompts.
  • Chains: An ouptut-chain is an input-chain for the another chain, generating a better and more complete data for final ouput