Large Language Models

Application Of Large-Scale Pre-trained Models In Quantitative Investment (Part 1)

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Application Of Large-Scale Pre-trained Models In Quantitative Investment (Part 1)

Application of Large-Scale Pre-trained Models in Quantitative Investment (Part 1) Research Unit: Taiping Asset Management Co., Ltd. Project Leader: Wang Zhenzhou Project Team Members: Wang Teng, Yi Chao, Zuo Wenting, Hu Qiang, Yu Hui Abstract: This project deeply explores the application of large-scale pre-trained models in the field of quantitative investment, mainly addressing several key … Read more

Collection of 10 Outstanding Large Language Model Papers

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Collection of 10 Outstanding Large Language Model Papers

Large Language Models (LLMs) are a type of artificial intelligence model designed to understand and generate human language. They are trained on vast amounts of text data and can perform a wide range of tasks, including text summarization, translation, sentiment analysis, and more. LLMs are characterized by their large scale, containing billions of parameters, which … Read more

Artificial Intelligence: What Are Large Models?

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Welcome to the special winter vacation column “High-Tech Lessons for Kids” launched by Science Popularization China! As one of the cutting-edge technologies today, artificial intelligence is changing our lives at an astonishing pace. From smart voice assistants to self-driving cars, from AI painting to machine learning, it opens up a future full of infinite possibilities. … Read more

Multi-Agent Collaboration Mechanism: A Review of Large Language Models

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Multi-Agent Collaboration Mechanism: A Review of Large Language Models

Source: ZHUAN ZHI This article is approximately 6000 words long and suggests a reading time of 12 minutes. This article provides a comprehensive overview of collaboration in multi-agent systems and proposes a scalable framework to guide future research. With the latest advancements in large language models (LLMs), Agentic Artificial Intelligence (Agentic AI) has made significant … Read more

In-Depth Analysis of ChatGPT’s Development, Principles, Architecture, and Future

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In-Depth Analysis of ChatGPT's Development, Principles, Architecture, and Future

Source: Dolphin Data Science Laboratory This article is approximately 6000 words and is recommended for a 12-minute read. This is a deep technical popular science and interpretation article, without excessive technical terms. [ Introduction ] The author of this article is Dr. Chen Wei, who previously served as the chief scientist of a Huawei-affiliated natural … Read more

Comprehensive Summary of Large Language Model Evaluation Methods

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Comprehensive Summary of Large Language Model Evaluation Methods

Source: Algorithm Advancement This article is approximately 8900 words long and is recommended to be read in 9 minutes. This article comprehensively introduces evaluation methods for large language models. Since the introduction of the Transformer model in 2017, research in natural language processing has gradually shifted towards pre-trained models based on this framework, such as … Read more

Educational Applications of Large Language Models: Principles, Status, and Challenges

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Educational Applications of Large Language Models: Principles, Status, and Challenges

Abstract: Large Language Models (LLMs) are natural language processing technologies used to describe vast amounts of text through vector representations and generative probabilities. Recently, with the emergence of representative products like ChatGPT, which has garnered widespread attention in the education sector due to its excellent capabilities in generation, comprehension, logical reasoning, and dialogue, research on … Read more

Google & Hugging Face: The Strongest Language Model Architecture for Zero-Shot Capability

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Google & Hugging Face: The Strongest Language Model Architecture for Zero-Shot Capability

This article is approximately 2000 words long and takes about 5 minutes to read. If the goal is the model's zero-shot generalization capability, the decoder structure + language model task is the best; if multitask finetuning is also needed, the encoder-decoder structure + MLM task is the best. From GPT-3 to prompts, more and more … Read more

CMU Liu Pengfei: The Fourth Paradigm of NLP

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CMU Liu Pengfei: The Fourth Paradigm of NLP

Written by | Liu Pengfei Edited by | Jia Wei Source | AI Technology Review In the past two years, the research paradigm based on pre-training + fine-tuning has rapidly swept the entire field of NLP. This research paradigm is widely recognized as a revolutionary paradigm in NLP research, with previous paradigms including “expert systems,” … Read more

Precise Induction of Language Model Knowledge Through Prompt Construction

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Precise Induction of Language Model Knowledge Through Prompt Construction

NLP Paradigm Evolution Fully Supervised Learning (Non-neural Network): Trains a specific task model only on the input-output sample dataset for the target task, heavily relying on feature engineering. Fully Supervised Learning (Neural Network): Combines feature learning with model training, shifting the research focus to architecture engineering, which designs a network architecture (like CNN, RNN, Transformer) … Read more