Black-Box Prompt Optimization (BPO) addresses the challenge of aligning Large Language Models (LLMs) with human preferences without requiring model retraining. It offers a novel approach for users seeking to improve LLM output quality and safety by optimizing prompts.

How It Works

BPO operates by treating prompt engineering as an optimization problem. It leverages a separate preference model to iteratively refine prompts based on pairwise comparisons of LLM responses. This black-box approach avoids direct model fine-tuning, making it applicable to proprietary LLMs and reducing computational overhead.

Quick Start & Requirements

• Install dependencies: pip install -r requirements.txt
• Requires Python and PyTorch. GPU with CUDA is recommended for efficient inference and training.
• Official demo available on Hugging Face.
• Inference example provided in the README.

Highlighted Details

• Outperforms PPO and DPO in aligning LLMs like GPT-3.5-turbo and Claude-2.
• Achieves orthogonal improvements in LLM alignment.
• Released model and dataset on Hugging Face.
• Includes code for data construction, model training, inference, and evaluation.

Maintenance & Community

• Project associated with ACL 2024.
• Codebase references and acknowledges contributions from llm_finetuning, DeepSpeed-Chat, and LLaMA-Factory.

Licensing & Compatibility

• The README does not explicitly state a license. The presence of code from other projects with varying licenses may imply specific usage terms.

Limitations & Caveats

The project is presented as research code, and the README includes #TODO comments indicating areas requiring user modification before execution, suggesting it may not be fully production-ready.