FIPO: Free-form Instruction-oriented Prompt Optimization with Preference Dataset and Modular Fine-tuning Schema
This work addresses the need for cost-effective and generalizable prompt optimization in AI, though it is incremental as it builds on existing automatic prompt optimization techniques.
The paper tackles the problem of expensive expert-based prompt optimization for large language models by introducing FIPO, a method that uses a large-scale Prompt Optimization Preference dataset and modular fine-tuning to optimize prompts in a model-agnostic way, achieving improved performance across multiple benchmarks and models.
When the quality of naive prompts is carefully optimized by human experts, the task performance of large language models (LLMs) can be significantly improved. However, expert-based prompt optimizations are expensive. Herein, some works have proposed Automatic Prompt Optimization (APO), to optimize naive prompts according to task outputs of given in-box testing models, with the help of advanced LLMs (e.g., GPT-4) in an ad-hoc way. Although effective, existing schemes suffer from poor generalization ability and privacy risk. To this end, we collect the first large-scale Prompt Optimization Preference dataset (POP), fine-tune offline local LLM-based optimizers, then fairly test with various downstream models. Our method allows accurate optimization of the core task instruction part within the naive prompt in a model-agnostic manner, and thus is named Free-from Instruction-oriented Prompt Optimization (FIPO). In specific, FIPO uses a modular APO template that dynamically integrate the naive task instruction, optional instruction responses, and optional ground truth to produce finely optimized prompts. The POP dataset is meticulously constructed using advanced LLMs, undergoing rigorous cross-validation by human experts and analytical models. Leveraging insights from the data with Tulu2 models and diverse fine-tuning strategies, we validate the efficacy of FIPO framework across five public benchmarks and six testing models. Check codes and data here: https://github.com/LuJunru/FIPO_Project.