This repo provides the source code & data of our paper: Unleashing the Potential of Large Language Models as Prompt Optimizers: An Analogical Analysis with Gradient-based Model Optimizers.

Highlights:

• 1️⃣ We are the first to conduct a systematic study for LLM-based prompt optimizers.
• 💡 By drawing inspiration from gradient-based model optimization techniques, we develop a capable Gradient-inspired LLM based Prompt Optimizer called GPO.
• 🔝 GPO brings an additional improvement of up to 56.8% on Big-Bench Hard and 55.3% on MMLU compared to baseline methods.

We propose a novel perspective to investigate the design of LLM-based prompt optimizers, by drawing an analogy with gradient-based model optimizers. By systematically analyzing a rich set of improvement strategies, we further develop a capable Gradient-inspired LLM-based Prompt Optimizer called GPO. At each step, it first retrieves relevant prompts from the optimization trajectory as the update direction. Then, it utilizes the generation-based refinement strategy to perform the update, while controlling the edit distance through a cosine-based decay strategy.

• python == 3.10.13
• vllm == 0.2.7
• transformers == 4.36.2
• sentence-transformers == 2.2.2
• openai == 0.28.0
• rouge == 1.0.1
• nltk == 3.8.1
• torch == 2.1.2

• openai_api_key: The OpenAI API key. Required for accessing OpenAI's API services.
• gpus: Specifies the list of GPUs to be used. If multiple GPUs are provided, the model will utilize them.

• dataset: The dataset used for optimizing prompts (options: bbh, mmlu, gsm8k, webnlg, wsc).
• task_name: The tasks used for optimizing prompts. Use "all" to include all tasks.

• optimizer_llm_name: Name of the prompt optimizer LLM.
• optimizer_temperature: Temperature setting of the optimizer.

• initial_instruction: Initial prompt for the optimization process.
• num_search_epochs: Number of epochs to optimize the prompt.
• opt_batch_size: Batch size for the optimization process.
• instruction_pos: Position of the prompt in the optimization process (options: before_Q, Q_begin, Q_end,A_begin).
• num_generated_instructions_in_each_step: Number of prompts generated in each optimization step.
• gradient_name: Name of gradient used in prompt optimization (options: -, feedback).
• momentum_para_name: Type of momentum used for optimization (options: -, para, feedback).
• momentum_selection_name: Method of the momentum selection used for optimization (options: -, recency, relavance, importance).
• momentum_selection_num: Number of momentum selections to use for optimization.
• momentum_update_name: Method of the momentum update used for optimization (options: -, k-list, real-time).
• learning_rate_name: Learning rate strategy used for optimization (options: w_lr, wo_lr).
• initial_step_size: Initial step size for optimization.
• decay_strategy: Type of decay strategy to apply (options: fixed, linear, cosine).
• use_warmup_strategy: Whether to use a warmup strategy for optimization.
• warmup_steps: Number of steps for the warmup process.
• final_step_size: Final step size after decay.
• util_gradient_name: Name of the utilized gradient for optimization (options: edit, generate, edit_without, generate_without).
• format_data_num: Number of format data of utilizing gradient to optimize the prompt.

• scorer_llm_name: Name of the task LLM.
• include_qa: Whether to include QA pairs in the prompt.
• only_evaluate: Whether the process should only evaluate without optimization.
• evaluate_generated_ins_on_few_shot: Whether to evaluate the generated prompts on a few-shot setting.
• few_shot_number: Number of demonstrations to include in the few-shot evaluation.

After completing the parameter settings above, you can conduct prompt optimization by executing the following script.

python src/optimization/main.py

In our experiments, we use GPO.sh as the script for our prompt optimization framework.

bash GPO.sh

The prompts optimized by our LLM-based prompt optimizer GPO are in the GPO_results.jsonl

You can download the our immediate results from the link.