We are working on using a large language model to develop prompts to provide a structured review and parameter extraction of a paper.
This can greatly ease the review process by automating parts of method extraction.
We are working on extracting the the analysis parameters similar to the COBIDAS, specifically working on the parameters as part of the Carp (2012) paper.
We'll be working with LangChain and Ollama to test different prompts and models.
We'll learn how to interact with different LLMs while building LangChain prompts to extract specific information or otherwise summarize the technical writing of an article.
Data to use
No response
Number of collaborators
more
Credit to collaborators
We'll track contributions and decide what kind of product for the hackathon (or other future work) as we go.
Hopefully, we can get to a small demo for NeuroLibre
Image
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Type
other
Development status
0_concept_no_content
Topic
reproducible_scientific_methods
Tools
other
Programming language
Python
Modalities
other, not_applicable
Git skills
1_commit_push
Anything else?
No response
Things to do after the project is submitted and ready to review.
[ ] Add a comment below the main post of your issue saying: Hi @brainhackorg/project-monitors my project is ready!
Title
reviewer2go
Leaders
Brent McPherson Kendra Oudyk
Collaborators
No response
Brainhack Global 2023 Event
Brainhack Montreal
Project Description
Link to project repository/sources
https://github.com/bcmcpher/reviewer2go
Goals for Brainhack Global
Create a sequence of prompts that can systematically extract model parameters and features from an article to facilitate the review process.
Good first issues
Based on a journal's review process, write a prompt chain to evaluate how ready an article is to publish / how well its analyses are described.
Based on a specific review papers criteria, write
Communication channels
https://github.com/bcmcpher/reviewer2go
Skills
Onboarding documentation
No response
What will participants learn?
We'll learn how to interact with different LLMs while building LangChain prompts to extract specific information or otherwise summarize the technical writing of an article.
Data to use
No response
Number of collaborators
more
Credit to collaborators
We'll track contributions and decide what kind of product for the hackathon (or other future work) as we go.
Hopefully, we can get to a small demo for NeuroLibre
Image
Leave this text if you don't have an image yet.
Type
other
Development status
0_concept_no_content
Topic
reproducible_scientific_methods
Tools
other
Programming language
Python
Modalities
other, not_applicable
Git skills
1_commit_push
Anything else?
No response
Things to do after the project is submitted and ready to review.
Hi @brainhackorg/project-monitors my project is ready!