Organizing a Society of Language Models: Structures and Mechanisms for Enhanced Collective Intelligence

• URL: http://arxiv.org/abs/2405.03825v1
• Date: Mon, 6 May 2024 20:15:45 GMT
• Title: Organizing a Society of Language Models: Structures and Mechanisms for Enhanced Collective Intelligence
• Authors: Silvan Ferreira, Ivanovitch Silva, Allan Martins,
• Abstract summary: This paper introduces a transformative approach by organizing Large Language Models into community-based structures. We investigate different organizational models-hierarchical, flat, dynamic, and federated-each presenting unique benefits and challenges for collaborative AI systems. The implementation of such communities holds substantial promise for improve problem-solving capabilities in AI.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent developments in Large Language Models (LLMs) have significantly expanded their applications across various domains. However, the effectiveness of LLMs is often constrained when operating individually in complex environments. This paper introduces a transformative approach by organizing LLMs into community-based structures, aimed at enhancing their collective intelligence and problem-solving capabilities. We investigate different organizational models-hierarchical, flat, dynamic, and federated-each presenting unique benefits and challenges for collaborative AI systems. Within these structured communities, LLMs are designed to specialize in distinct cognitive tasks, employ advanced interaction mechanisms such as direct communication, voting systems, and market-based approaches, and dynamically adjust their governance structures to meet changing demands. The implementation of such communities holds substantial promise for improve problem-solving capabilities in AI, prompting an in-depth examination of their ethical considerations, management strategies, and scalability potential. This position paper seeks to lay the groundwork for future research, advocating a paradigm shift from isolated to synergistic operational frameworks in AI research and application.

Related papers

• Agentic LLM Framework for Adaptive Decision Discourse [2.4919169815423743]
  This study introduces a real-world inspired agentic Large Language Models (LLMs) framework. Unlike traditional decision-support tools, the framework emphasizes dialogue, trade-off exploration, and the emergent synergies generated by interactions among agents. Results reveal how the breadth-first exploration of alternatives fosters robust and equitable recommendation pathways.
  arXiv  Detail & Related papers  (2025-02-16T03:46:37Z)
• Multi-Agent Collaboration Mechanisms: A Survey of LLMs [6.545098975181273]
  Multi-Agent Systems (MASs) enable groups of intelligent agents to coordinate and solve complex tasks collectively. This work provides an extensive survey of the collaborative aspect of MASs and introduces a framework to guide future research.
  arXiv  Detail & Related papers  (2025-01-10T19:56:50Z)
• Cognitive LLMs: Towards Integrating Cognitive Architectures and Large Language Models for Manufacturing Decision-making [51.737762570776006]
  LLM-ACTR is a novel neuro-symbolic architecture that provides human-aligned and versatile decision-making. Our framework extracts and embeds knowledge of ACT-R's internal decision-making process as latent neural representations. Our experiments on novel Design for Manufacturing tasks show both improved task performance as well as improved grounded decision-making capability.
  arXiv  Detail & Related papers  (2024-08-17T11:49:53Z)
• Efficient Adaptation in Mixed-Motive Environments via Hierarchical Opponent Modeling and Planning [51.52387511006586]
  We propose Hierarchical Opponent modeling and Planning (HOP), a novel multi-agent decision-making algorithm. HOP is hierarchically composed of two modules: an opponent modeling module that infers others' goals and learns corresponding goal-conditioned policies. HOP exhibits superior few-shot adaptation capabilities when interacting with various unseen agents, and excels in self-play scenarios.
  arXiv  Detail & Related papers  (2024-06-12T08:48:06Z)
• Transforming Competition into Collaboration: The Revolutionary Role of Multi-Agent Systems and Language Models in Modern Organizations [0.0]
  This article explores the influence of computational entities based on multi-agent systems theory (SMA) and large language models (LLM) on human user interaction. In our approach we employ agents developed from large language models (LLM), each with distinct prototyping that considers behavioral elements. We demonstrate the potential of developing agents useful for organizational strategies, based on multi-agent system theories (SMA) and innovative uses based on large language models (LLM based)
  arXiv  Detail & Related papers  (2024-03-12T15:56:10Z)
• Semantic Computing for Organizational Effectiveness: From Organization Theory to Practice through Semantics-Based Modelling [0.0]
  Key features of our model include inferable dependencies, explainable coordination and cooperation risks, and actionable insights on how dependency structures within an organization can be altered to mitigate the risks. Our approach underscores the transformative potential of semantics in deriving tangible, real-world value from existing organization theory.
  arXiv  Detail & Related papers  (2023-12-29T19:37:35Z)
• Balancing Autonomy and Alignment: A Multi-Dimensional Taxonomy for Autonomous LLM-powered Multi-Agent Architectures [0.0]
  Large language models (LLMs) have revolutionized the field of artificial intelligence, endowing it with sophisticated language understanding and generation capabilities. This paper proposes a comprehensive multi-dimensional taxonomy to analyze how autonomous LLM-powered multi-agent systems balance the dynamic interplay between autonomy and alignment.
  arXiv  Detail & Related papers  (2023-10-05T16:37:29Z)
• Exploring Collaboration Mechanisms for LLM Agents: A Social Psychology View [60.80731090755224]
  This paper probes the collaboration mechanisms among contemporary NLP systems by practical experiments with theoretical insights. We fabricate four unique societies' comprised of LLM agents, where each agent is characterized by a specific trait' (easy-going or overconfident) and engages in collaboration with a distinct thinking pattern' (debate or reflection) Our results further illustrate that LLM agents manifest human-like social behaviors, such as conformity and consensus reaching, mirroring social psychology theories.
  arXiv  Detail & Related papers  (2023-10-03T15:05:52Z)
• Corex: Pushing the Boundaries of Complex Reasoning through Multi-Model Collaboration [83.4031923134958]
  Corex is a suite of novel general-purpose strategies that transform Large Language Models into autonomous agents. Inspired by human behaviors, Corex is constituted by diverse collaboration paradigms including Debate, Review, and Retrieve modes. We demonstrate that orchestrating multiple LLMs to work in concert yields substantially better performance compared to existing methods.
  arXiv  Detail & Related papers  (2023-09-30T07:11:39Z)
• Self-organizing Democratized Learning: Towards Large-scale Distributed Learning Systems [71.14339738190202]
  democratized learning (Dem-AI) lays out a holistic philosophy with underlying principles for building large-scale distributed and democratized machine learning systems. Inspired by Dem-AI philosophy, a novel distributed learning approach is proposed in this paper. The proposed algorithms demonstrate better results in the generalization performance of learning models in agents compared to the conventional FL algorithms.
  arXiv  Detail & Related papers  (2020-07-07T08:34:48Z)
• Distributed and Democratized Learning: Philosophy and Research Challenges [80.39805582015133]
  We propose a novel design philosophy called democratized learning (Dem-AI) Inspired by the societal groups of humans, the specialized groups of learning agents in the proposed Dem-AI system are self-organized in a hierarchical structure to collectively perform learning tasks more efficiently. We present a reference design as a guideline to realize future Dem-AI systems, inspired by various interdisciplinary fields.
  arXiv  Detail & Related papers  (2020-03-18T08:45:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.