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Unleashing the Potential of Large Language Models as Prompt Optimizers: An Analogical Analysis with Gradient-based Model Optimizers

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.

😀 Overview

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.

🚀 Quick Start

Requirements

  • 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

Parameter Settings

Basic Configuration

  • 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 and Task Configuration

  • 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 Configuration

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

Prompt Optimization Configuration

  • 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.

Evaluation Configuration

  • 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.

Prompt Optimization

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

🌟 Results

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.

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