Prompting Frameworks for Large Language Models: A Survey

The novelty of this paper lies in the introduction and comprehensive survey of Prompting Frameworks (PFs) for Large Language Models (LLMs). The paper categorizes existing PFs into three classes, provides a comparative analysis of their strengths and limitations, and discusses practical recommendations for their effective use.

From a broader standpoint, the paper examines the evolution of language models up to current LLMs, their limitations—such as temporal lag and token processing capacity—and the constraints imposed by their inability to directly interact with the external world. The paper highlights Prompting Frameworks as a mechanism to overcome these limitations by serving as a bridge between LLMs and external applications, enabling LLMs to access real-time information, process lengthy documents, and integrate with external services.

In terms of commercial applications, the concepts and tools discussed could have profound implications across industries that rely on language models. Prompting Frameworks could serve as a foundational layer for developing applications that require AI systems to exhibit nuanced communication capabilities and interact with a broader digital ecosystem. Possible applications might include advanced chatbots, personalized educational platforms, decision support systems in healthcare, contextual analysis tools in finance and law, and creative content generation in marketing and media.

The paper’s merit is grounded in its systematic presentation of a novel concept and its role in enhancing the practical capabilities of LLMs. It also offers a repository of related works and tools, as noted by the authors, to facilitate further research.

The paper presents an extensive review of Prompting Frameworks for Large Language Models (LLMs) and introduces a hierarchical structure to systematically understand the interplay between LLMs and these frameworks.

Findings and Observations:

• The authors provide a definition for Prompting Framework (PF) emphasizing its four essential properties: modularity, abstraction, extensibility, and standardization. These properties ensure easy management, simplified interfaces, customization capabilities, and improved maintainability.
• They categorize the workflow into four hierarchical layers:
  • Data Level: Handles data transmission and preprocessing, interacting with external data sources and ensuring various data types are appropriately processed for LLMs.
  • Base Level: Serves as a computational hub, taking responsibility for the management of LLMs and supporting knowledge management and decision-making processes.
  • Execute Level: Engages with the business logic, interacting with LLMs to accomplish specific real-world tasks. It includes three parts: direct utilization of LLMs’ raw outputs, invoking external models for complex tasks, and coordinating with higher-order models or agents for advanced AI applications.
  • Service Level: Manages, schedules, and integrates advanced tasks within the business workflow.

Conclusions:

• The paper argues that by leveraging PFs, the capabilities of LLMs can be enhanced, thereby helping to mitigate LLMs’ inherent limitations such as temporal lag and constrained token processing capacity.
• It underlines the significant potential for PFs to transform the interactions between LLMs and various industries, thus acting as a facilitator for bringing to life more sophisticated use cases and real-world applications of AI.

Table 1 in the document lists representative works of prompting frameworks, classifying them into three categories and several subcategories such as “The Shell of LLMs (LLM-SH),” “Language for Interaction with LLMs (LLM-LNG),” and “Output Restrictors of LLMs (LLM-RSTR),” which represent different approaches to extending LLMs functionalities.

The paper hints at these frameworks enabling a breadth of applications, including the creation of intelligent autonomous agents and furthering the development of Artificial General Intelligence (AGI).

Commercial Relevance:

• The different domains listed, such as education, healthcare, finance, legal, retail, and more, underscore the broad commercial relevance of LLMs enabled by PFs. Removing the barrier between LLMs and external entities means that enterprises could adopt AI to conduct sophisticated analysis, automate complex tasks, and create highly interactive and adaptive systems.
• Representative works mentioned could serve as direct resources for businesses seeking to implement such frameworks in their AI strategies. The way different frameworks specialize in different aspects such as modularity or extensibility points towards a growing ecosystem of customizable solutions that enterprises can leverage based on their specific needs.