BARRED: generate faithful and diverse training sets
Unofficial implementation of the BARRED paper:
Boundary Alignment Refinement through REflection and Debate, aka BARRED arXiv:2604.25203
What is BARRED?
In a nutshell: BARRED is a framework for generating faithful and diverse synthetic training data using only a task description and a small set of unlabeled examples.
Step by step:
- Define the task and give examples to know what to generate Criterion:
Criterion: `True means this sentence is positive, False otherwise` Examples: `I love it`, `it's rainy`, `My uncle bobby is a good boy` - Let the library decompound the problem into dimensions
- Then the library generates the training set
- Results: a set of labeled sentences
1. `My mother is a wonderful person -> true` 2. `Her sister is boring -> false`
Why it's cool?
TODO
Installation
uv add barred
# with Pip
pip install barred
Usage
TODO
Development setup
Install after cloning the repository:
just install
Check everything (lint, type, tests...):
just checks
1 maintainer
Metadata
1 maintainer
Release files for barred 0.2.0
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