[论文解读] A Survey on Large Language Models from Concept to Implementation

Chen Wang, Jin Zhao+1 more|arXiv (Cornell University)|Mar 27, 2024

Topic Modeling被引用 5

一句话总结

这是对 Transformer 基于的大型语言模型（LLMs）的全面综述，涵盖它们的文本到图像能力、图像 captioning、以及跨领域应用，并讨论架构、混合模型与市场趋势。

ABSTRACT

Recent advancements in Large Language Models (LLMs), particularly those built on Transformer architectures, have significantly broadened the scope of natural language processing (NLP) applications, transcending their initial use in chatbot technology. This paper investigates the multifaceted applications of these models, with an emphasis on the GPT series. This exploration focuses on the transformative impact of artificial intelligence (AI) driven tools in revolutionizing traditional tasks like coding and problem-solving, while also paving new paths in research and development across diverse industries. From code interpretation and image captioning to facilitating the construction of interactive systems and advancing computational domains, Transformer models exemplify a synergy of deep learning, data analysis, and neural network design. This survey provides an in-depth look at the latest research in Transformer models, highlighting their versatility and the potential they hold for transforming diverse application sectors, thereby offering readers a comprehensive understanding of the current and future landscape of Transformer-based LLMs in practical applications.

研究动机与目标

• Survey the evolution and architectures of Transformer-based LLMs (e.g., GPT series, PaLM) and their capabilities.
• Analyze text-to-image and image captioning models, including Prior/Decoder architectures and diffusion/GAN approaches.
• Discuss cross-domain applications, integration with other technologies, and potential future directions and challenges.
• Highlight market trends and industrial impacts of LLMs across NLP, CV, and related fields.

提出的方法

• Literature review of Transformer-based LLMs and key models (GPT-3.5, GPT-4, PaLM, Bard) and comparison on inference, math, multitasking, and NL generation.
• Analysis of text-to-image pipelines (Prior/Decoder, three-Transformer vector space) and architectures like Disco Diffusion, Imagen, CLIP, DALL-E, StyleGAN.
• Examination of image captioning approaches (GAN-based LEMON, CLIP-based retrieval, diffusion-based synthesis) and attention mechanisms.
• Discussion of hybrid image-to-text models (SmallCap, retrieval-augmented, reinforcement learning variants) and their trade-offs.
• Evaluation of cross-modal capabilities (text-to-image, image understanding, knowledge graphs) and implications for interactive systems.

实验结果

研究问题

• RQ1 What are the key Transformer-based architectures behind current LLMs and their relative strengths/limitations?
• RQ2 How do text-to-image pipelines (Prior/Decoder) translate textual prompts into images, and what are their main components?
• RQ3 What are the main approaches to image captioning and caption quality limitations in current models?
• RQ4 What are the hybrid and cross-modal strategies that enhance image-text understanding and generation?
• RQ5 What are the market and industry implications and future challenges for LLM-driven technologies?

主要发现

• GPT-3.5-Turbo reportedly has 20 billion parameters, indicating improved efficiency over GPT-3.5.
• Task performance tends to improve with increased training requirements, suggesting a shift toward qualitative enhancements alongside scaling.
• CLIP provides image-text alignment in a shared semantic space but has limitations in style understanding, artistic nuance, and sentiment control.
• Text-to-image models like DALL-E and Imagen demonstrate advances in detail and realism via transformer-based and diffusion-based architectures.
• Generative AI and LLM markets are projected to grow substantially, with LLMs expected to reach hundreds of billions in value by 2032.
• Hybrid models combining pre-trained encoders, cross-attention, and reinforcement learning offer efficiency and adaptability benefits.]
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