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

• URL: http://arxiv.org/abs/2501.07815v1
• Date: Tue, 14 Jan 2025 03:26:43 GMT
• Title: Agent-Centric Projection of Prompting Techniques and Implications for Synthetic Training Data for Large Language Models
• Authors: Dhruv Dhamani, Mary Lou Maher,
• Abstract summary: This paper introduces and explains the concepts of linear contexts (a single, continuous sequence of interactions) and non-linear contexts (branching or multi-path) in Large Language Models (LLMs)<n>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.
• Score: 0.8879149917735942
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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