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

Data

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.

Language

English

Performance

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:

python

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()

print(resp)

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.

Citation

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

@article{cheng2023black,
  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},
  year={2023}
}

THUDM/BPO

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

Black-box Prompt Optimization (BPO)