Agent-Centric Projection of Prompting Techniques and Implications for Synthetic Training Data for Large Language Models

Recent advances in prompting techniques and multi-agent systems for Large Language Models (LLMs) have produced increasingly complex approaches. However, we lack a framework for characterizing and comparing prompting techniques or understanding their relationship to multi-agent LLM systems. This position paper introduces and explains the concepts of linear contexts (a single, continuous sequence of interactions) and non-linear contexts (branching or multi-path) in LLM systems. These concepts enable the development of an agent-centric projection of prompting techniques, a framework that can reveal deep connections between prompting strategies and multi-agent systems. We propose three conjectures based on this framework: (1) results from non-linear prompting techniques can predict outcomes in equivalent multi-agent systems, (2) multi-agent system architectures can be replicated through single-LLM prompting techniques that simulate equivalent interaction patterns, and (3) these equivalences suggest novel approaches for generating synthetic training data. We argue that this perspective enables systematic cross-pollination of research findings between prompting and multi-agent domains, while providing new directions for improving both the design and training of future LLM systems.

The Tyranny of Possibilities in the Design of Task-Oriented LLM Systems: A Scoping Survey

This scoping survey focuses on our current understanding of the design space for task-oriented LLM systems and elaborates on definitions and relationships among the available design parameters. The paper begins by defining a minimal task-oriented LLM system and exploring the design space of such systems through a thought experiment contemplating the performance of diverse LLM system configurations (involving single LLMs, single LLM-based agents, and multiple LLM-based agent systems) on a complex software development task and hypothesizes the results. We discuss a pattern in our results and formulate them into three conjectures. While these conjectures may be partly based on faulty assumptions, they provide a starting point for future research. The paper then surveys a select few design parameters: covering and organizing research in LLM augmentation, prompting techniques, and uncertainty estimation, and discussing their significance. The paper notes the lack of focus on computational and energy efficiency in evaluating research in these areas. Our survey findings provide a basis for developing the concept of linear and non-linear contexts, which we define and use to enable an agent-centric projection of prompting techniques providing a lens through which prompting techniques can be viewed as multi-agent systems. The paper discusses the implications of this lens, for the cross-pollination of research between LLM prompting and LLM-based multi-agent systems; and also, for the generation of synthetic training data based on existing prompting techniques in research. In all, the scoping survey presents seven conjectures that can help guide future research efforts.