Understanding Scientific Papers With Prompt Engineering & The ChatGPT API
AI Nuggets
Detailed Instructions, CLI Commands, Website URLs, and Tips from Video Transcript
Setting Up the Environment
Use a notebook for logging and registering understanding.
Set up the ChatGPT API for use with GPT-4 Turbo.
Using the ChatGPT API
Define the default system message:
"You're a helpful research and programming assistant."
Understanding the Paper
The paper being analyzed is titled “Towards a Human in the Loop LM Approach to Collaborative Discourse Analysis.”
The content of the paper is copied into a string variable named paper_contents.
Generating Prompts
Create a function to generate prompts based on high-level questions:
def prompt_template(paper_questions, paper_contents: str) -> str: # ... function body ...
Use the following high-level questions to generate prompts:
What problem is the paper trying to solve?
Why is this problem interesting?
What is the primary contribution?
How do they do it?
What are the key takeaways from the paper?
Modifying Prompts for Citations
Modify the prompts to ask the model to return citations from the paper that validate the statement:
"At the end of your answer, include at least one citation from the original paper to validate your response."
Iterating Over Prompts
Loop over the generated prompts and collect the outputs:
for prompt in prompt_high_level_question_list: # ... code to collect outputs ...
Displaying Outputs
Use Markdown to display the output of the first question, for example:
output_high_level_question[0]
Potential Extensions
Outline background knowledge required to understand a paper.
Quiz oneself on the paper.
Extract citations and quotes from the paper.
Summarize in different formats.
Discuss the paper with the model.
Ask the model to give feedback on the learner’s understanding based on a section of the paper.
Tips and Observations
The approach can be scaled to validate complex outputs from the model regarding a paper.
It’s possible to cross-check citations mentioned by the model against the actual paper.
Prompt engineering is effective when thinking in terms of input-output pairs that can be developed with a model.
Additional Information
The video is based on a lecture note from CS267 at Stanford University by Tim Roughgarden, which can be found at:
cs.stanford.edu
The specific URL for the video or additional resources was not provided in the transcript. Please visit the provided link to access the video and any related materials:
http://youtube.com/watch?v=sMpEZmA0zpE
(Note: The above outline is based solely on the provided transcript and does not contain any additional information or commands that may be present in the video or its description.)