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

2025-01-14

AI Summary

Existing research lacks a systematic characterization of large language model (LLM) prompting techniques—particularly their formal relationship with multi-agent systems (MAS) and the impact of prompting strategies on synthetic training data quality. Method: We propose an “agent-centric prompting technique projection framework” that establishes, for the first time, a formal mapping between prompting strategies and MAS. We introduce linear and nonlinear context concepts to uncover deep equivalences between single-model prompting and multi-agent collaboration, and formulate three core theoretical conjectures, substantiated via conceptual modeling, contextual structure analysis, and equivalence reasoning. Contribution/Results: The framework provides a unified theoretical foundation for both LLM prompting design and MAS simulation. It further inspires a novel paradigm for controllable, prompt-guided synthetic data generation—enhancing data quality through principled prompting strategies.

Technology Category

• Natural Language Processing: Prompt Engineering / Prompting
• Multiagent Systems: Modeling other Agents
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

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.

Problem

• Large Language Models
• Prompting Techniques
• Multi-Agent Systems

Innovation

• Nonlinear Prompting
• Multi-Agent Systems
• Language Model Simulation