GReaTer: Gradient-Based Prompt Optimization for Smaller Language Models

The performance of large language models (LLMs) is closely linked to the design of prompts. Prompt optimization is therefore crucial for improving the performance of these models in a variety of tasks. While large, commercial LLMs often achieve impressive results, optimizing prompts for smaller, open-source models presents a challenge. A new approach called GReaTer (Gradients over Reasoning) promises a remedy by directly incorporating gradient information about task-specific reasoning.

The Challenge of Prompt Optimization

Existing methods for automated prompt optimization are often based on textual feedback. Prompts are refined based on inference errors identified by large LLMs. However, smaller models have difficulty generating high-quality feedback, leading to a complete dependence on evaluation by large LLMs. Furthermore, these methods do not utilize more direct and fine-grained information such as gradients, as they operate exclusively in the text space.

GReaTer: A New Approach

GReaTer takes an innovative approach by directly integrating gradient information about task-specific reasoning into prompt optimization. By leveraging loss gradients, GReaTer enables self-optimization of prompts for open-source language models without relying on expensive, large LLMs. This allows for powerful prompt optimization without dependence on massive LLMs and closes the gap between smaller models and the complex reasoning often required for prompt refinement.

How GReaTer Works

GReaTer analyzes the gradients of the loss function with respect to the tokens used in the prompt. These gradients provide insights into how changes in the prompt affect the model's performance. By iteratively adjusting the prompt along these gradients, the model's performance is gradually improved. In contrast to purely text-based methods, GReaTer thus makes more direct use of the information present within the model itself.

Evaluation and Results

Extensive evaluations across various reasoning tasks, including BBH, GSM8k, and FOLIO, show that GReaTer consistently outperforms previous state-of-the-art prompt optimization methods, even those that rely on powerful LLMs. Moreover, prompts optimized with GReaTer often demonstrate better transferability and, in some cases, boost task performance to a level comparable to or even exceeding that of larger language models. This underscores the effectiveness of gradient-based prompt optimization.

Outlook and Significance

GReaTer opens up new possibilities for the efficient use of smaller language models. By avoiding dependence on large, resource-intensive LLMs, prompt optimization becomes accessible to a wider audience. The improved transferability of the optimized prompts further contributes to usability and dissemination. GReaTer thus represents an important contribution to the democratization of AI technologies and enables smaller companies and research institutions to benefit from advances in the field of language models.

GReaTer and Mindverse

For a company like Mindverse, which specializes in the development of AI-powered content tools, GReaTer offers exciting possibilities. The technology could be integrated into the platform to enable the automated creation and optimization of prompts for various applications. This would help Mindverse users maximize the performance of their language models and generate higher-quality content. The integration of GReaTer could provide Mindverse with a competitive advantage and strengthen the company's position as a leading provider of AI-powered content solutions.

Bibliographie - Das, S. S. S., Kamoi, R., Pang, B., Zhang, Y., Xiong, C., & Zhang, R. (2024). GReaTer: Gradients over Reasoning Makes Smaller Language Models Strong Prompt Optimizers. arXiv preprint arXiv:2412.09722. - Cui, A., & Nandyalam, P. (2024). Introducing MAPO: Momentum-Aided Gradient Descent Prompt Optimization. arXiv preprint arXiv: (to be filled). - Sabbatella, A., & Ponti, A. (2024). Prompt Optimization in Large Language Models. Mathematics, 12(6), 929. - Tang, X., Wang, X., Zhao, W. X., Lu, S., Li, Y., & Wen, J.-R. (2024). Unleashing the Potential of Large Language Models as Prompt Optimizers: An Analogical Analysis with Gradient-based Model Optimizers. arXiv preprint arXiv:2402.17564v1.