Efficient Prompting Methods for Large Language Models: A Survey
(2404.01077)Abstract
Prompting has become a mainstream paradigm for adapting LLMs to specific natural language processing tasks. While this approach opens the door to in-context learning of LLMs, it brings the additional computational burden of model inference and human effort of manual-designed prompts, particularly when using lengthy and complex prompts to guide and control the behavior of LLMs. As a result, the LLM field has seen a remarkable surge in efficient prompting methods. In this paper, we present a comprehensive overview of these methods. At a high level, efficient prompting methods can broadly be categorized into two approaches: prompting with efficient computation and prompting with efficient design. The former involves various ways of compressing prompts, and the latter employs techniques for automatic prompt optimization. We present the basic concepts of prompting, review the advances for efficient prompting, and highlight future research directions.
Overview
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This survey discusses the enhancement of LLMs through efficient prompting methods, focusing on reducing computational demand and manual effort.
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It categorizes efficient prompting into two main approaches: computation efficiency and design efficiency, exploring techniques like knowledge distillation, encoding, and optimization.
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Methods such as knowledge distillation, encoding techniques, and filtering reduce the prompt size while retaining essential information, enhancing LLM performance.
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The survey anticipates the future of efficient prompting in LLMs, emphasizing multi-objective optimization to balance prompt compression and task accuracy.
Efficient Prompting Methods for LLMs: A Comprehensive Survey
Introduction to Efficient Prompting
The adaptation of LLMs to specific tasks through prompts has significantly enhanced their utility in NLP. However, the creation and utilization of detailed and extensive prompts introduce computational and human effort overheads. This survey categorizes methods to increase the efficiency of prompts into two main approaches: prompting with efficient computation and prompting with efficient design. The goal is to mitigate the computational burden and the manual effort required in designing effective prompts for LLMs.
Computation Efficiency in Prompting
Methods focusing on computation efficiency seek to compress prompts to alleviate the computational demands of model inference. These methodologies can broadly be subdivided into three categories:
Knowledge Distillation
Knowledge Distillation (KD) techniques compress detailed prompts into more compact forms that retain the essential information needed for the LLMs to perform tasks effectively. This is achieved by training a lighter "student" model to mimic the "teacher" model's performance while using the compressed prompts. Significant work in this area has demonstrated the feasibility of distilling complex prompts into simpler, compressed versions without losing the effectiveness of the original prompts.
Encoding Techniques
Encoding methods compress prompt information into vectors, thereby reducing the length of prompts needed for LLMs to understand and execute tasks. These approaches vary from encoding image information into special tokens to creating imaginary word-based "X-Prompts" that encapsulate specific styles or contexts incompressible through ordinary language. The encoding strategies provide a novel way of representing complex prompt information in a more LLM-accessible form.
Filtering Retractable Information
The filtering approach focuses on reducing the prompt length by identifying and removing redundant or less useful information from the prompts. Selective context and iterative compression algorithms highlight the utility of evaluating the information entropy within prompts to distill essential content. By prioritizing the most informative parts of a prompt, these methods enhance the LLM's focus on relevant information, thus optimizing both memory usage and computational speed.
Design Efficiency in Prompting
Efficient design methods automate the optimization of prompts, relieving the human effort necessary for manual prompt creation.
Gradient-based Optimization
These methods employ gradients to fine-tune prompts in both open-source and closed-source LLMs. Techniques range from real-gradient tuning, which involves modifying soft prompts through gradient descent, to imitated-gradient prompting where NLP tasks guide prompt optimization in the absence of direct model access.
Evolution-based Optimization
Evolutionary algorithms offer a framework for prompt optimization by simulating natural selection processes. Recent methodologies leverage LLMs themselves as part of the evolutionary operators, iterating over prompt generations to refine task performance through a "survival of the fittest" approach for prompt selection.
Future Directions and Theoretical Perspectives
The survey introduces efficient prompting as a multi-objective optimization problem, aiming to compress prompts and optimize LLM task accuracy simultaneously. This theoretical abstraction opens pathways for future research endeavors focusing on information filtering, fine-tuning accessible parameters, and exploring the co-optimization of prompts in various semantic spaces.
Conclusion
This comprehensive survey highlights the significant strides made in developing efficient prompting methods for LLMs. By categorizing and evaluating the approaches based on computation and design efficiencies, the survey sheds light on promising directions for future research. Moreover, the reconciliation of efficient prompting methods with ongoing advancements in LLMs poses an interesting avenue for exploration, aiming for the seamless integration of human-like understanding and interaction capabilities within AI systems.
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