A Layered Architecture for Developing and Enhancing Capabilities in Large Language Model-based Software Systems

• URL: http://arxiv.org/abs/2411.12357v1
• Date: Tue, 19 Nov 2024 09:18:20 GMT
• Title: A Layered Architecture for Developing and Enhancing Capabilities in Large Language Model-based Software Systems
• Authors: Dawen Zhang, Xiwei Xu, Chen Wang, Zhenchang Xing, Robert Mao,
• Abstract summary: This paper introduces a layered architecture that organizes Large Language Models (LLMs) software system development into distinct layers. By aligning capabilities with these layers, the framework encourages the systematic implementation of capabilities in effective and efficient ways.
• Score: 18.615283725693494
• License:
• Abstract: Significant efforts has been made to expand the use of Large Language Models (LLMs) beyond basic language tasks. While the generalizability and versatility of LLMs have enabled widespread adoption, evolving demands in application development often exceed their native capabilities. Meeting these demands may involve a diverse set of methods, such as enhancing creativity through either inference temperature adjustments or creativity-provoking prompts. Selecting the right approach is critical, as different methods lead to trade-offs in engineering complexity, scalability, and operational costs. This paper introduces a layered architecture that organizes LLM software system development into distinct layers, each characterized by specific attributes. By aligning capabilities with these layers, the framework encourages the systematic implementation of capabilities in effective and efficient ways that ultimately supports desired functionalities and qualities. Through practical case studies, we illustrate the utility of the framework. This work offers developers actionable insights for selecting suitable technologies in LLM-based software system development, promoting robustness and scalability.

Related papers

• APT: Architectural Planning and Text-to-Blueprint Construction Using Large Language Models for Open-World Agents [8.479128275067742]
  We present an advanced Large Language Model (LLM)-driven framework that enables autonomous agents to construct complex structures in Minecraft. By employing chain-of-thought decomposition along with multimodal inputs, the framework generates detailed architectural layouts and blueprints. Our agent incorporates both memory and reflection modules to facilitate lifelong learning, adaptive refinement, and error correction throughout the building process.
  arXiv  Detail & Related papers  (2024-11-26T09:31:28Z)
• 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)
• 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)
• A Comprehensive Review of Multimodal Large Language Models: Performance and Challenges Across Different Tasks [74.52259252807191]
  Multimodal Large Language Models (MLLMs) address the complexities of real-world applications far beyond the capabilities of single-modality systems. This paper systematically sorts out the applications of MLLM in multimodal tasks such as natural language, vision, and audio.
  arXiv  Detail & Related papers  (2024-08-02T15:14:53Z)
• Meta Reasoning for Large Language Models [58.87183757029041]
  We introduce Meta-Reasoning Prompting (MRP), a novel and efficient system prompting method for large language models (LLMs) MRP guides LLMs to dynamically select and apply different reasoning methods based on the specific requirements of each task. We evaluate the effectiveness of MRP through comprehensive benchmarks.
  arXiv  Detail & Related papers  (2024-06-17T16:14:11Z)
• Large Language Model Agent as a Mechanical Designer [7.136205674624813]
  In this study, we present a novel approach that integrates pre-trained LLMs with a FEM module. The FEM module evaluates each design and provides essential feedback, guiding the LLMs to continuously learn, plan, generate, and optimize designs without the need for domain-specific training. Our results reveal that these LLM-based agents can successfully generate truss designs that comply with natural language specifications with a success rate of up to 90%, which varies according to the applied constraints.
  arXiv  Detail & Related papers  (2024-04-26T16:41:24Z)
• LLMs as On-demand Customizable Service [8.440060524215378]
  We introduce a concept of hierarchical, distributed Large Language Models (LLMs) By introducing a "layered" approach, the proposed architecture enables on-demand accessibility to LLMs as a customizable service. We envision that the concept of hierarchical LLM will empower extensive, crowd-sourced user bases to harness the capabilities of LLMs.
  arXiv  Detail & Related papers  (2024-01-29T21:24:10Z)
• If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents [81.60906807941188]
  Large language models (LLMs) are trained on a combination of natural language and formal language (code) Code translates high-level goals into executable steps, featuring standard syntax, logical consistency, abstraction, and modularity.
  arXiv  Detail & Related papers  (2024-01-01T16:51:20Z)
• MAgIC: Investigation of Large Language Model Powered Multi-Agent in Cognition, Adaptability, Rationality and Collaboration [102.41118020705876]
  Large Language Models (LLMs) have marked a significant advancement in the field of natural language processing. As their applications extend into multi-agent environments, a need has arisen for a comprehensive evaluation framework. This work introduces a novel benchmarking framework specifically tailored to assess LLMs within multi-agent settings.
  arXiv  Detail & Related papers  (2023-11-14T21:46:27Z)
• CRAFT: Customizing LLMs by Creating and Retrieving from Specialized Toolsets [75.64181719386497]
  We present CRAFT, a tool creation and retrieval framework for large language models (LLMs) It creates toolsets specifically curated for the tasks and equips LLMs with a component that retrieves tools from these sets to enhance their capability to solve complex tasks. Our method is designed to be flexible and offers a plug-and-play approach to adapt off-the-shelf LLMs to unseen domains and modalities, without any finetuning.
  arXiv  Detail & Related papers  (2023-09-29T17:40:26Z)
• Multi-Agent Collaboration: Harnessing the Power of Intelligent LLM Agents [0.0]
  We present a novel framework for enhancing the capabilities of large language models (LLMs) by leveraging the power of multi-agent systems. Our framework introduces a collaborative environment where multiple intelligent agent components, each with distinctive attributes and roles, work together to handle complex tasks more efficiently and effectively.
  arXiv  Detail & Related papers  (2023-06-05T23:55:37Z)

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.