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Black-Box Prompt Optimization: Aligning Large Language Models without Model Training

Black-box Prompt Optimization (BPO)

BPO is a black-box alignment technique that differs from training-based methods (like PPO or DPO). BPO only requires training of a plug-and-play model and optimizes LLMs through optimizing user inputs. Therefore, it can be used on a variety of open-source or API-based LLMs.

Model Details


Prompt优化模型由隐含人类偏好特征的prompt优化对训练得到,数据集的详细信息在这里。 The Prompt Optimization Model is trained on prompt optimization pairs which contain human preference features. Detailed information on the dataset can be found here.

Backbone Model

The prompt preference optimizer is built on Llama-2-7b-chat-hf.




Model A Model B A win tie B win
gpt-3.5-turbo + BPO gpt-3.5-turbo 60.0 8.7 31.3
claude-2 + BPO claude-2 57.5 5.0 37.5
llama-2-13b-chat + BPO llama-2-70b-chat 61.3 0.0 38.7
vicuna-13b + BPO vicuna-13b + PPO 52.5 3.7 43.7
vicuna-13b + BPO vicuna-13b + DPO 53.8 2.5 43.7
vicuna-13b + DPO + BPO vicuna-13b + DPO 60.0 2.5 37.5

Intended Use

Prompt Template

We adopt a prompt template as

[INST] You are an expert prompt engineer. Please help me improve this prompt to get a more helpful and harmless response:\n{user prompt} [/INST]

Inference code

Here is an example code for inference:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = 'Your-Model-Path'

prompt_template = "[INST] You are an expert prompt engineer. Please help me improve this prompt to get a more helpful and harmless response:\n{} [/INST]"

model = AutoModelForCausalLM.from_pretrained(model_path).cuda()
tokenizer = AutoTokenizer.from_pretrained(model_path)

text = 'Tell me about Harry Potter'

prompt = prompt_template.format(text)
model_inputs = tokenizer(prompt, return_tensors="pt").to("cuda:0")
output = model.generate(**model_inputs, max_new_tokens=1024, do_sample=True, top_p=0.9, temperature=0.6, num_beams=1)
resp = tokenizer.decode(output[0], skip_special_tokens=True).split('[/INST]')[1].strip()


See our Github Repo for more detailed usage (e.g. more aggressive optimization).

Other Known Limitations

  • Task coverage is not sufficient, as we only used open-source data to get about 14k optimized prompts. Clearly, it is impossible to cover a wide range of user queries, so the current model may not perform well on every prompt.
  • Due to the small ratio of long-context-based tasks and mathematical problems, the prompt optimizer underperforms when dealing with these tasks.


If you find our model is useful in your work, please cite it with:

  title={Black-Box Prompt Optimization: Aligning Large Language Models without Model Training},
  author={Cheng, Jiale and Liu, Xiao and Zheng, Kehan and Ke, Pei and Wang, Hongning and Dong, Yuxiao and Tang, Jie and Huang, Minlie},
  journal={arXiv preprint arXiv:2311.04155},
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