The Internet of Large Language Models: An Orchestration Framework for LLM Training and Knowledge Exchange Toward Artificial General Intelligence

• URL: http://arxiv.org/abs/2501.06471v1
• Date: Sat, 11 Jan 2025 08:00:24 GMT
• Title: The Internet of Large Language Models: An Orchestration Framework for LLM Training and Knowledge Exchange Toward Artificial General Intelligence
• Authors: Wilson Wei, Nicholas Chen, Yuxuan Li,
• Abstract summary: This paper explores the multi-dimensional challenges faced during the development of Large Language Models (LLMs) To address these challenges, this paper proposes three core technical solutions: LLM sharing protocol, LLM universal environment framework, and Agent optimal path module.
• Score: 4.403567403521342
• License:
• Abstract: This paper explores the multi-dimensional challenges faced during the development of Large Language Models (LLMs), including the massive scale of model parameters and file sizes, the complexity of development environment configuration, the singularity of model functionality, and the high costs of computational resources. To address these challenges, this paper proposes three core technical solutions: LLM sharing protocol, LLM universal environment framework, and Agent optimal path module. To solve the computational resource constraints in the early stages of research, we further innovatively propose a joint mining mechanism, achieving bilateral value sharing between computing power providers and model designers, including breakthrough rewards for optimal model paths and long-term profit distribution, thereby providing researchers with cost-optimized computational resource support and promoting the continuous development of LLM research and applications.

Related papers

• Enhancing LLM Reasoning with Reward-guided Tree Search [95.06503095273395]
  o1-like reasoning approach is challenging, and researchers have been making various attempts to advance this open area of research. We present a preliminary exploration into enhancing the reasoning abilities of LLMs through reward-guided tree search algorithms.
  arXiv  Detail & Related papers  (2024-11-18T16:15:17Z)
• A Survey: Collaborative Hardware and Software Design in the Era of Large Language Models [16.250856588632637]
  The rapid development of large language models (LLMs) has significantly transformed the field of artificial intelligence. These models are increasingly integrated into diverse applications, impacting both research and industry. This paper surveys hardware and software co-design approaches specifically tailored to address the unique characteristics and constraints of large language models.
  arXiv  Detail & Related papers  (2024-10-08T21:46:52Z)
• EVOLvE: Evaluating and Optimizing LLMs For Exploration [76.66831821738927]
  Large language models (LLMs) remain under-studied in scenarios requiring optimal decision-making under uncertainty. We measure LLMs' (in)ability to make optimal decisions in bandits, a state-less reinforcement learning setting relevant to many applications. Motivated by the existence of optimal exploration algorithms, we propose efficient ways to integrate this algorithmic knowledge into LLMs.
  arXiv  Detail & Related papers  (2024-10-08T17:54:03Z)
• Retrieval-Enhanced Machine Learning: Synthesis and Opportunities [60.34182805429511]
  Retrieval-enhancement can be extended to a broader spectrum of machine learning (ML) This work introduces a formal framework of this paradigm, Retrieval-Enhanced Machine Learning (REML), by synthesizing the literature in various domains in ML with consistent notations which is missing from the current literature. The goal of this work is to equip researchers across various disciplines with a comprehensive, formally structured framework of retrieval-enhanced models, thereby fostering interdisciplinary future research.
  arXiv  Detail & Related papers  (2024-07-17T20:01:21Z)
• Solution-oriented Agent-based Models Generation with Verifier-assisted Iterative In-context Learning [10.67134969207797]
  Agent-based models (ABMs) stand as an essential paradigm for proposing and validating hypothetical solutions or policies. Large language models (LLMs) encapsulating cross-domain knowledge and programming proficiency could potentially alleviate the difficulty of this process. We present SAGE, a general solution-oriented ABM generation framework designed for automatic modeling and generating solutions for targeted problems.
  arXiv  Detail & Related papers  (2024-02-04T07:59:06Z)
• Large Multi-Modal Models (LMMs) as Universal Foundation Models for AI-Native Wireless Systems [57.41621687431203]
  Large language models (LLMs) and foundation models have been recently touted as a game-changer for 6G systems. This paper presents a comprehensive vision on how to design universal foundation models tailored towards the deployment of artificial intelligence (AI)-native networks.
  arXiv  Detail & Related papers  (2024-01-30T00:21:41Z)
• A Survey of Resource-efficient LLM and Multimodal Foundation Models [22.23967603206849]
  Large foundation models, including large language models (LLMs), vision transformers (ViTs), diffusion, and multimodal models, are revolutionizing the entire machine learning lifecycle. However, the substantial advancements in versatility and performance these models offer come at a significant cost in terms of hardware resources. This survey delves into the critical importance of such research, examining both algorithmic and systemic aspects.
  arXiv  Detail & Related papers  (2024-01-16T03:35:26Z)
• Machine Learning Insides OptVerse AI Solver: Design Principles and Applications [74.67495900436728]
  We present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI solver. We showcase our methods for generating complex SAT and MILP instances utilizing generative models that mirror multifaceted structures of real-world problem. We detail the incorporation of state-of-the-art parameter tuning algorithms which markedly elevate solver performance.
  arXiv  Detail & Related papers  (2024-01-11T15:02:15Z)
• Beyond Efficiency: A Systematic Survey of Resource-Efficient Large Language Models [32.774929826684854]
  Large Language Models (LLMs) bring forth challenges in the high consumption of computational, memory, energy, and financial resources. This survey aims to systematically address these challenges by reviewing a broad spectrum of techniques designed to enhance the resource efficiency of LLMs.
  arXiv  Detail & Related papers  (2024-01-01T01:12:42Z)
• The Efficiency Spectrum of Large Language Models: An Algorithmic Survey [54.19942426544731]
  The rapid growth of Large Language Models (LLMs) has been a driving force in transforming various domains. This paper examines the multi-faceted dimensions of efficiency essential for the end-to-end algorithmic development of LLMs.
  arXiv  Detail & Related papers  (2023-12-01T16:00:25Z)
• Do Generative Large Language Models need billions of parameters? [0.0]
  The research explores novel methods that allow different parts of the model to share parameters. This approach ensures that the model remains compact without sacrificing its ability to learn and represent complex language structures.
  arXiv  Detail & Related papers  (2023-09-12T20:25:22Z)

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.