Self-Refine: LLM research paper for iterative output refinement
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This repository provides a framework for iterative self-refinement in Large Language Models (LLMs), enabling them to generate feedback on their own outputs and use that feedback to improve subsequent results. It's designed for researchers and developers exploring LLM capabilities in tasks requiring iterative improvement and self-correction.
How It Works
The core approach involves a generator LLM producing an initial output. This output is then critiqued by one or more "critic" LLMs, generating feedback. This feedback, along with the original output, is fed back to a "refiner" LLM, which produces an improved version. This cycle repeats until a stopping criterion is met, allowing for progressive enhancement of the output.
Quick Start & Requirements
prompt-lib
by cloning the repository and running pip install prompt-lib/
.PYTHONPATH
to include the cloned prompt-lib
directory.codenet-python-train.jsonl.zip
for code readability).Highlighted Details
Maintenance & Community
The project is associated with authors from various institutions, as indicated by the citation. Further community or maintenance details are not explicitly provided in the README.
Licensing & Compatibility
The repository does not explicitly state a license. The citation lists the paper as arXiv:2303.17651. Users should verify licensing for commercial or closed-source use.
Limitations & Caveats
The setup requires manual cloning and configuration of prompt-lib
and potentially setting PYTHONPATH
. Some tasks require downloading or unzipping specific datasets before execution. The README does not specify the LLM models used or their API requirements.
10 months ago
1+ week