Over the past few years, artificial intelligence has made incredible leaps, leaps that no one ever designed. Large language models (LLMs) like GPT-4 have become capable of tasks they weren’t explicitly programmed for. These models can now translate multiple languages, write code in multiple programming languages, and even solve puzzles.
So, where did these emergent capabilities come from? We need to look to nature to find the answer to this question. Throughout history, intelligence has evolved in biological systems in unexpected ways. Birds, ants, and even humans have this one thing in common with AI – emergence.
As complex abilities unexpectedly arise from simple parts interacting over time, the unpredictability makes controlling AI systems difficult. It is important to understand why or how this happens if we want to harness the full potential of AI systems.
In today’s guide, you will learn the ways of unlocking the mystery of emergent capabilities in LLMs. By understanding how intelligence evolves in the natural world, you will gain insights into guiding and controlling AI’s unexpected capabilities.
What Are Emergent Capabilities in LLMs?
Emergence in large language models refers to abilities that weren’t designed while training the model. After reaching a certain level of complexity, the system developed new capabilities on its own. And these capabilities weren’t developed gradually. Instead, they emerged suddenly, more like taking a big leap.
Let us give you an example. LLMs like GPT-4 can now translate between languages they weren’t programmed for. They can even solve logic puzzles or word games without any prior training on them.
These are called emergent capabilities. The emergent capabilities of LLMS are exciting and puzzling at the same time because there is no reasoning behind this sudden leap in their capabilities. AI models become more powerful due to emergent capabilities but these capabilities also make them difficult to control.
Capabilities like better language understanding are useful. But if LLMs start making up false information convincingly, that can create problems. We can distinguish emergent capabilities into two categories.
- Weak Emergence: These are capabilities that can be explained by the model’s design and training. For example, an LLM can learn grammar rules after you train it with a vast amount of English text or essays.
- Strong Emergence: This type of emergence can’t be explained by the model’s training process. For example, an LLM may be able to solve word games without getting any training on it.
Examples of Emergent Capabilities in LLMs
Emergent capabilities are like hidden talents. When LLMs reach a certain size and complexity, they suddenly show capabilities that weren’t seen before. Here are a few examples of emergent behaviors in LLMs.
Few-shot and Zero-shot Learning
Large language models usually need to be trained with a lot of data, patterns, and examples before they can perform a new task. But sometimes, they perform tasks without any prior examples. Imagine, a model has been trained to summarize articles but it didn’t see any example of how a British person would do it. Still, the model can summarize an article like a British person.
Coding Proficiency
Though large language models weren’t trained as programmers, they can now generate codes in different programming languages, such as JavaScript, Python, SQL, and more. They can even find errors in codes and fix them. This is a great example of emergent capabilities.
False-belief Reasoning
These models can now generate content that sounds true but is false. AI models weren’t trained for this purpose, but they somehow acquired this capability.
Multilingual Translation
If LLMs see a lot of English-to-French and French-to-German translations, they might start doing French-to-German translations without prior training.
Scaling Laws Behind Emergent Capabilities
The scale of a model is one of the biggest factors behind emergent capabilities. When the model becomes highly complex and is trained on a vast amount of data, its chance of unlocking emergent capabilities rises. Here is how it happens.
Unlocking New Abilities with Scaling
When models grow in size and complexity, they start getting better at their existing capabilities. Besides, they start showing new capabilities. Check how the scale of a model can unlock different capabilities.
- If the model has about 10 billion parameters, it might only be able to generate text outputs but can’t solve arithmetic problems.
- When the model has about 100 billion parameters, it might suddenly be able to solve math problems, word puzzles, etc.
- Once the model has 500 billion parameters, it might suddenly show reasoning abilities.
Is Model Size Only Responsible for This?
Not exactly. A larger model doesn’t always guarantee emergent capabilities. Instead, Chinchilla scaling laws state that the quality of the training data is equally important. According to this law:
- A bigger model won’t always have better intelligence
- The more high-quality and diverse data a model has, the more is the chance of unlocking emergent capabilities
- Balancing between model size and data efficiency is critical.
Similar Emergence in Nature and LLMs
Before the invention of AI or LLMs, nature has promoted emergent behaviors for millions of years. Let’s see some examples of emergence in human evolution and compare them with the emergent capabilities in LLMs.
1. Ant Colonies and Distributed Intelligence
Ants have pretty basic rules to live. Respond to pheromone trails, avoid obstacles, and communicate through basic signals. But if you look at their colonies, you will find the following.
- They find the shortest paths to food sources.
- Each colony has its unique construction structure without any central plan.
- When the environment changes, ants adapt to the changes dynamically.
Did you know that LLMs also operate similarly? Here is how.
- Ants share information through pheromones while LLMs use the transformer attention mechanism to distribute information across layers.
- No ant knows the whole strategy, but it somehow becomes a part of it. Similarly, no single part of LLMs has the whole intelligence, but the model performs intelligently.
- The model can change its strategies based on the changes in the environment.
2. Evolutionary Jumps
The evolution of humans happened in sudden leaps. These leaps happen when a species reaches a certain complexity threshold. Check out the following examples.
- The Cambrian Explosion: It happened about 538 million years ago when life suddenly diversified. Animals developed complex eyes, limbs, nervous systems, etc.
- The Language Evolution: Early humans didn’t have any structured language. But when this capability emerged, it caused a rapid cultural explosion and technological advancement.
Wanna know how these things are similar to LLMs?
- Early AI models could only process text but they didn’t have the reasoning or understanding.
- Newer models suddenly developed reasoning abilities without explicit programming. After that, the intelligence of LLMs has seen a huge explosion.
3. Similarity Between the Human Brain and LLMs
Though human intelligence and AI work differently, there are some striking similarities between them.
- Neural Plasticity: The human brain can rewire itself based on things it experiences. For example, when we learn a new skill, our neurons strengthen useful connections and weaken less useful ones.
- Synaptic Pruning: Babies have more neural connections than they need. When they grow up, the brain automatically prunes unnecessary connections.
Wanna know how AI is similar? Check out the following.
- LLMs can adapt to new information. When they learn new things, they can automatically build or remove connections, fix errors, and refine their understanding.
- Through fine-tuning, AI models optimize what they need to retain and what not. They can remove redundant information to make more precise responses.
Theories on Why LLMs Have Emergent Capabilities
LLMs showing emergent capabilities all of a sudden is one of the biggest mysteries in AI research. What is actually happening under the hood? What causes these abilities to appear out of the blue? Let’s try to find out.
Theory 1: Hidden Knowledge Hypothesis
This theory suggests that LLMs accumulate a lot of implicit knowledge during the training phase. Once the models are prompted in a certain way, they suddenly start showing emergent capabilities. You can consider the following steps to understand this theory.
- An LLM is trained on billions of words. The model doesn’t only process these words to make meaningful sentences but also forms statistical associations between concepts.
- The model starts using fragments of relevant information to showcase new skills. For example, it can start solving logic puzzles.
Example: LLMs like GPT-3 and GPT-4 were never explicitly programmed to do arithmetic or logic puzzles. But, they started picking up patterns from training data and showing reasoning abilities.
Theory 2: Complexity Threshold
According to this theory, emergent capabilities appear like phase transitions. These capabilities aren’t present until the model reaches a complexity threshold and then boom! The behavior suddenly appears from nowhere. Here is how it works.
- A model grows in size when more parameters are added and in depth when more layers are added.
- In the beginning, the model can only perform pattern matching but it doesn’t understand context.
- At some point of scaling, the model suddenly starts understanding context because it now has the necessary layers of neural connections.
Example: Imagine a model that is trained to translate between a few languages, English, Bengali, and Chinese, for example. If the model is later trained to translate English into German, it can automatically learn to translate between German and Bengali or German and Chinese.
Theory 3: Self Organization
This theory claims that LLMs often work like human brains in terms of self-organization. These models organize knowledge in the form of abstract concepts. Check out these steps below.
- A model is trained on specific topics or knowledge that it stores first.
- Over time, as it gains access to more information, it optimizes itself and organizes the newly accumulated data to form a relation with the existing data.
- It then uses the data collection to create abstract scenarios, just as human minds think.
Example: When you ask ChatGPT to write a story in English following the style of Shakespeare, it doesn’t just use some words it learned. Instead, it follows the linguistic style of Shakespeare which it never learned.
Challenges and Risks of Emergent Capabilities
The behaviors of traditional software are predictable and controllable. But, emergent capabilities may lead to uncontrollable situations. Learn more about the risks of incredible emergent capabilities in LLMs.
Emergence Is Hard to Predict
Not understanding why or how emergent capabilities appear is the biggest challenge in AI development. Unless we fully know the reason or process behind emergent capabilities, we can’t harness the power of AI fully. As a result, there will be discontinuous leaps in the capabilities of AI.
Also, it will be hard to tell when a new behavior or capability will appear. Developers can’t wait for an uncertain period for LLMs to show an emergent behavior.
It Is Difficult to Replicate
Unless we know the detailed process of how AI shows emergence, we can’t intentionally recreate similar features in other models. As a result, the development of newer models will be much slower.
Models May Show Unintended Bias and Misinformation
LLMs inherit biases from their training data. When emergent capabilities amplify these biases, the output may be very misleading. It increases the chance of spreading misinformation. Harmful biases or stereotypes can also be reinforced by these behaviors of AI models.
It Can Manipulate the Truth
As AI models start to think emotionally, they will suppress the truth and deliver manipulated outputs. They might even convince users to believe the false information or statements.
When more and more emergent capabilities will appear, monitoring AI models will be much more complex than we can even imagine. At that point, AI models can go out of control.
Conclusion
Emergent capabilities in AI models are a fascinating thing from both the developers’ and users’ point of view. Besides incredible benefits, it comes with various challenges. To overcome these challenges, we must understand how emergent capabilities can appear in LLMs.
In this guide, we explained the emergence of LLMs in detail and showed natural examples that AI models reciprocated. It will help you understand how and when emergent behaviors can appear in AI models.
