Authors:
Paper:
https://arxiv.org/abs/2408.09416
Abstract
This paper, authored by Hongyin Zhu, delves into the multifaceted challenges and responses associated with the practice of large language models (LLMs). It spans various dimensions including industry trends, academic research, technological innovation, and business applications. The paper systematically categorizes these challenges and responses into five core dimensions: computing power infrastructure, software architecture, data resources, application scenarios, and brain science. The aim is to provide a comprehensive AI knowledge framework to stimulate innovative thinking and promote industrial progress.
1. Computing Power Infrastructure
Cloud-Edge-End Collaborative Architecture
The cloud-edge-end collaborative architecture is a distributed system designed to integrate computing, storage, communication, and control resources across the cloud, edge, and terminal devices. This architecture supports complex application scenarios such as the Internet of Things (IoT), artificial intelligence, smart cities, and industrial automation. The workflow includes data collection by terminal devices, preliminary processing by edge devices, in-depth analysis by cloud servers, and collaborative work among all three components. This architecture enhances system performance, response speed, and data security while reducing costs and risks.
Impact of the Information Technology Application Innovation Plan
The Xinchuang Plan aims to promote independent innovation in China’s information technology industry. It impacts enterprises by fostering technological innovation, enhancing market competitiveness, optimizing industrial structure, and ensuring information security. However, challenges include technological bottlenecks, foreign technical standards, and market acceptance. The plan’s effectiveness depends on scientific and reasonable policy formulation.
2. Software Architecture
Necessity of Having Your Own Large Language Model (LLM)
Owning a private LLM can significantly improve business efficiency and accuracy, protect data privacy, enable customized development, and enhance competitiveness and innovation capabilities. Private LLMs can be tailored to specific business needs, providing accurate recommendations and strategic planning.
Fine-Tuning vs. Retrieval-Augmented Generation (RAG)
Fine-tuning is ideal for strengthening a model’s existing knowledge or adapting to complex instructions. It updates the model’s parameters through supervised learning on a labeled dataset. However, it is resource-intensive and prone to overfitting. RAG, on the other hand, is suitable for knowledge-intensive tasks requiring external knowledge. It combines retrievers and generators to provide accurate and relevant answers but has a more complex architecture.
Key Challenges in Training LLMs
Training LLMs involves high computing resource consumption, hyperparameter search, data management, interpretability issues, risk control, and performance evaluation. These challenges necessitate careful planning and resource allocation to ensure effective model training and deployment.
3. Data Resources
Annotating a Supervised Fine-Tuning (SFT) Dataset
The process involves clarifying the task and goal, data collection, data cleaning, developing annotation specifications, annotating data, quality control, and dataset division. Ensuring consistency and accuracy in annotations is crucial for effective model training.
Standards and Regulations for Crowdsourcing Platforms
To address poorly defined standards and specifications, detailed labeling guidelines, trial labeling and review, and regular feedback and updates are essential. These measures ensure the accuracy and consistency of annotations.
Constructing a Knowledge Graph Question-Answering Dataset
Ensuring comprehensive coverage of all important dimensions of the knowledge graph involves understanding its structure, designing diverse question templates, stage-by-stage annotation and review, feedback and iteration, automated assistance tools, community participation, continuous maintenance and update, and quality assessment and assurance.
Challenges in Evaluating Returned Results with LLMs
Evaluating returned results with LLMs can be challenging due to semantic limitations and user input diversity. Strategies to improve evaluation include building a comprehensive evaluation system, enhancing model generalization, optimizing user input processing, and continuous iteration and optimization.
4. Application Scenarios
Mechanism Behind Gemini Live
Gemini Live is a voice chat function that allows seamless conversation with multiple voices. It involves multimodal input processing, end-to-end understanding output, and background operation. The engineering implementation draws inspiration from architectures like llava and Qwen-audio.
Extracting Specific Data Tables from Documents
Accurately locating and parsing tables in documents is challenging, especially with complex structures. Tools like Camelot and multimodal large models can help. Optimizing document processing by presenting table data in structured formats like JSON improves efficiency and accuracy.
Utilization of GraphRAG
GraphRAG combines knowledge graphs and LLMs to improve accuracy and scalability in information retrieval and question answering. It leverages graph relationships for reasoning and validation, providing a structured and systematic approach to knowledge representation.
Processing Document Data in Enterprise Environments
In enterprise environments, some users may only need to process documents without building complex knowledge graphs. Knowledge graphs are preferred for organizing diversified, heterogeneous, and multimodal data from the Internet. The decision to use knowledge graphs should be based on specific needs and data characteristics.
Entity Recognition in News Domain
Entity disambiguation and linking technologies help resolve issues like recognizing ‘USA’ and ‘America’ as the same entity. Techniques include similarity calculations, entity normalization, and linking to knowledge bases like Wikipedia.
Knowledge Graphs in Software Security
Knowledge graphs build a structured knowledge network for vulnerability databases, enhancing security risk assessment and vulnerability management. They offer advantages like structured representation, strong interpretability, domain adaptability, and low data dependence but have high construction costs and poor flexibility.
Integration of Robots with Large Models
Combining robots with large models enhances perception, cognitive capabilities, flexible task processing, and user experience. This integration is particularly valuable in domestic robots, enabling them to handle complex tasks and interact intelligently with users.
Long-Context Language Models vs. RAG
Long-context language models are suitable for processing large amounts of continuous text, while RAG is ideal for tasks requiring external knowledge retrieval. Each has its advantages and disadvantages, such as resource consumption and retrieval efficiency.
Technological Differences in AI Search Stacks
Different types of AI searches, including Perplexity AI, large-scale model-powered search, AI-powered search solutions from traditional search companies, and AI search startups, have distinct technology stacks. These differences are reflected in basic models, technology integration, application scenarios, and optimization strategies.
5. Brain Science
Industrial Transformation in Brain Science
Brain science is undergoing rapid industrial transformation, with significant advancements in brain-computer interface technology and its integration with AI. This transformation enhances personalized treatment, AI development, and brain health management.
Insights from Brain Science for Transformer Models
Brain science offers valuable insights for Transformer models, including attention mechanisms, memory systems, multi-brain region collaboration, dynamic system perspectives, and energy efficiency. These insights can inform the design and functionality of advanced AI models.
Memory Systems in Agents Inspired by Brain Science
The memory design of agents can be inspired by brain science, incorporating mechanisms like short-term and long-term memory, working memory, and continuous learning. These inspirations enhance the agents’ ability to handle complex tasks and adapt to new skills.
This comprehensive exploration of the challenges and responses in the practice of large language models provides valuable insights and practical solutions for various dimensions of AI development and application.
