MAPO: Boosting Large Language Model Performance with Model-Adaptive Prompt Optimization (2407.04118v1)

Abstract: Prompt engineering, as an efficient and effective way to leverage LLMs (LLM), has drawn a lot of attention from the research community. The existing research primarily emphasizes the importance of adapting prompts to specific tasks, rather than specific LLMs. However, a good prompt is not solely defined by its wording, but also binds to the nature of the LLM in question. In this work, we first quantitatively demonstrate that different prompts should be adapted to different LLMs to enhance their capabilities across various downstream tasks in NLP. Then we novelly propose a model-adaptive prompt optimizer (MAPO) method that optimizes the original prompts for each specific LLM in downstream tasks. Extensive experiments indicate that the proposed method can effectively refine prompts for an LLM, leading to significant improvements over various downstream tasks.

• The paper’s main contribution is the introduction of MAPO, a framework that adapts prompts to each large language model to significantly enhance performance.
• It utilizes a multi-stage process including a warm-up dataset, supervised fine-tuning, and reinforcement learning with PPO and RRMF to optimize prompts.
• Empirical results across nine datasets show marked improvements in QA, classification, and generation tasks for models like BLOOM, GPT-J, and LLaMA.

Enhancing LLM Performance through Model-Adaptive Prompt Optimization

The paper "MAPO: Boosting LLM Performance with Model-Adaptive Prompt Optimization" presents a sophisticated approach for improving LLMs by optimizing prompts specific to each model. This work introduces a novel framework, MAPO, to achieve significant performance gains across various NLP tasks through Model-Adaptive Prompt Optimization.

Introduction

The motivation for this research hinges upon the realization that LLM performance on downstream tasks is heavily influenced by the quality of prompts. Traditionally, prompt optimization has focused on tailoring to specific tasks rather than individual LLMs. This paper quantitatively demonstrates that prompts should be adapted uniquely to each LLM to maximize their capabilities, a prospect largely unexplored in NLP.

Figure 1: Variance on answers from different LLMs (b) when they are given the same task-specific prompts (a).

Framework: Model-Adaptive Prompt Optimization (MAPO)

The core of MAPO involves several integral components:

1. Warm-up Dataset Establishment: This involves generating a significant number of candidate prompts from existing datasets using a sophisticated LLM (GPT-3.5 was utilized) to maintain the semantic essence while varying expressions. Prompts are then evaluated for optimal performance using a set heuristic against ground truth or outputs from a more powerful LLM.
2. Supervised Fine-Tuning (SFT): Conducting SFT on a variety of tasks allows the proposed system to generate responses that align with the preferences of LLMs, laying a foundational knowledge basis that is further enhanced by RL techniques.
3. Reward Model Construction: A reward model is trained to understand the effectiveness of prompts based on an LLM's preferences, utilized during reinforcement learning to finetune performance.
4. Reinforcement Learning: Combining Supervised Fine-Tuning with Reinforcement Learning via Proximal Policy Optimization (PPO) and RRMF ensures the model continually adapts and refines the prompt generation process.

   Figure 2: Framework of the proposed MAPO, including warm-up dataset establishment and prompt optimizer construction.

Empirical Results

The MAPO approach was tested across nine datasets spanning three tasks – question-answering (QA), classification, and generation. The results demonstrated superior adaptability and performance when compared with baseline models, as shown in detailed visual outputs and tabular datasets provided in the paper. The training showcased significant gains in robustness and task comprehension across models like BLOOM, GPT-J, and LLaMA.

Figure 3: The performance of different LLMs on task-specific prompts for three tasks: question-answering (a), classification (b), and generation (c). The results reveal significant variations across different LLMs' performance.

Performance Analysis

A detailed ablation paper revealed the interplay between SFT, PPO, and RRMF components. The reinforcement learning phase, complemented by PPO and RRMF, particularly stood out as a significant contributor to the MAPO's success, emphasizing adaptability and model-specific optimization in prompt generation.

Conclusion

The MAPO framework represents an important stride in adaptive LLM optimization, highlighting the potential for improved prompt generation strategies tailored to specific models. The results suggest further investigation into fine-grained model adaptation techniques, paving the way for future advancements in adaptive NLP systems and fine-tuning methodologies.

In sum, the research presented within this paper offers an effective paradigm shift in approaching prompt optimization in LLMs, showcasing the benefits of model-specific adaptations to drive performance improvements across diverse NLP tasks. As the field advances, the insights gained from this work will undoubtedly inform related domains, encouraging a broader adoption of adaptive LLM technologies.