Emergence refers to the phenomenon where, when the scale of the model and the amount of data reach a certain level, new abilities or characteristics spontaneously arise that were not previously evident. These abilities or characteristics are often not explicitly taught during the training process but emerge naturally through large amounts of data and complex network structures.
For example, in language models, when the number of parameters reaches a certain scale, the model may suddenly exhibit stronger language understanding capabilities and complex reasoning abilities.This “emergence” phenomenon indicates that large models can self-organize and learn complex patterns and rules during the training process, which may not appear in smaller models.
If we want to find a more easily understandable term, using “insight” might be more relatable. When the model parameters and training data reach a certain scale, it suddenly gains some abilities that exceed initial expectations.
Context learning, in English, is referred to as in-context learning, and is often translated as contextual learning. It refers to GPT’s ability to learn and understand tasks without providing any task-related training data during its training. Instead, it can learn and execute tasks through prompt instructions and/or examples, yielding expected results without the need for additional training.
I still use human learning and work as a metaphor. Suppose you have completed your education from elementary school to high school (pre-training), university major courses (supervised fine-tuning), and reinforcement learning (internship training), and are now officially entering the workforce. At this point, your supervisor will not provide you with specialized training but will directly assign work tasks. For example, if your supervisor explicitly asks you to help write an event planning proposal, but… you have indeed never done it or studied it before, your supervisor will likely give you a brief overview of the general idea of this plan and provide you with a few past event proposals as references. If you have good insight, you can learn from the reference examples and provide a decent event planning proposal.
With the support of parameter scale and training data, GPT possesses strong insight, allowing it to understand and solve various problems without additional training, as long as these problems can be inferred from contextual information. This ability enables GPT to demonstrate remarkable flexibility and adaptability in many natural language processing tasks; we only need to provide the necessary information in the prompt, and GPT can complete specific tasks.
The image above is a simple example of context learning. This is a small task of date format conversion, where there are no instructions about the task in the prompt, only two examples provided. From these two examples, GPT inferred what needed to be done and produced the expected result.
Reasoning, in English, is referred to as reasoning, and it is a key ability that GPT emerges after extensive learning. Reasoning ability refers to GPT’s capacity to identify useful clues from the input information, using existing knowledge and rules to provide reasonable answers or inferences. This is very similar to humans, who, after extensive learning and training, develop logical thinking abilities in our brains. When faced with more complex problems, we can think, identify issues, and propose reasonable solutions.
GPT, after large-scale training, also possesses strong reasoning abilities, enabling the large model to not only generate and understand language, but also conduct logical reasoning and solve problems. For example, when answering questions, recommending content, and participating in conversations, reasoning ability allows the model to provide more accurate and useful answers, thereby enhancing user experience and application effectiveness. Through reasoning ability, the large model can provide intelligent support in various fields such as education, healthcare, and law, greatly expanding its application scope and practical value.
There is a very important concept to mention:CoT (Chain of Thought), which is a method used to improve the model’s reasoning process. The basic idea is to allow the model to output a series of intermediate thought steps before generating the final answer. These steps can include:extracting key information from the question, performing intermediate calculations or transformations, and drawing conclusions based on previous steps, etc. This method simulates the thought process humans use to solve problems, analyzing and deducing the problem step by step, rather than just providing an answer directly. The following image illustrates what CoT is, with the highlighted text being the key to activating GPT’s chain of thought.
In AI Agents, GPT uses its reasoning ability to act as the brain of the agent, coordinating external tools to complete complex tasks. I will write a dedicated article later to introduce agent technology in detail.
Instruction following, in English, is referred to as instruction following, and can also be translated as instruction adherence. It refers to the model’s ability to understand and execute human natural language instructions. This ability enables the GPT model not only to generate coherent text but also to generate purposeful text or actions based on specific task requirements. For example, users can issue instructions to the model, asking it to summarize an article, translate a passage, solve a math problem, or create poetry, and GPT can generate corresponding output results based on the content of the instructions. This feature greatly expands the application scope of the GPT model, allowing it to play a role in various fields such as intelligent customer service, knowledge Q&A, and text creation.
GPT’s instruction following ability is trained through supervised fine-tuning and reinforcement learning, where a large number of examples with clear instructions and corresponding results are included in the training process, allowing the model to learn how to extract key information from input instructions and generate expected responses.
GPT has learned a vast amount of knowledge, compressing this knowledge into hundreds of billions of parameters, emerging with the ability to learn by analogy, complex problem reasoning ability, and understanding human instructions to produce results that meet human expectations. Therefore, the release of ChatGPT is considered a revolutionary breakthrough in artificial intelligence.The emergence of GPT means that machine learning models are no longer limited to rote memorization, but have begun to possess the ability to understand and create, which not only greatly expands the application boundaries of artificial intelligence but also provides infinite possibilities for the future of education, scientific research, art, and even the entire society’s intelligent transformation. As technology continues to evolve, GPT and its derivative models are expected to become a bridge connecting human wisdom and machine intelligence, leading us into a more intelligent, efficient, and creative new era.
