Emergent Abilities in Large Language Models

6 min readJun 9, 2023

Language models, particularly those in the GPT family, have experienced a fascinating evolution over the past six years. This progression is evident in how these models have been evaluated and benchmarked, shifting from using standard benchmark datasets to incorporating additional data sources for assessing capabilities not covered in traditional benchmarks. With GPT-4, OpenAI has introduced a System Card that outlines safety challenges, model limitations, capabilities, and potential implications users should be mindful of. Although these considerations apply to all generative models, the GPT-4 System Card serves as a valuable reference for evaluating the safety, addressing challenges, and managing unintended consequences arising from the models’ capabilities and limitations.

A key concern that stood out to me in the System Card was the emergent behaviour of these models. So, in this blog post, I’m going to break down what emergent behaviour in foundation models is all about, and what it means for app developers and organizations that use large foundation models in their products or organizational functions.

What is Emergent Behavior?

Emergent Behavior refers to unexpected patterns or behaviours that arise from the complex interactions within an AI model, rather than being explicitly programmed by the developers. Emergent behaviour can lead to novel outputs or insights, but it does not imply that the AI system has consciousness or human-like intelligence.

Let’s just be clear, Emergent Behavior is not AGI or Sentience or Singularity. Why? That’s beyond the scope of this post, but this page on Stochastic Parrots should give a sense of why it is not. Simply put, emergent behaviour represents models’ ability to infer and represent properties of the agent that produced the data by just having access to the text produced by human agents, with no direct evidence of the internal states of the agents that produced them.

Working at SolarWinds, I couldn’t stop myself from drawing an analogy from the Observability world. If observability is the ability to infer the internal states or conditions of a system based on its external outputs or behaviours, then emergent behaviour in foundational models can be seen as their ability to simulate a person’s behaviour using only the text generated by that person, without direct knowledge of the individual’s thoughts, emotions, or other underlying factors.

It’s like I binge-read Taleb, chatted with his pals, and suddenly, I’m a mini-Taleb! Hilarious, isn’t it? Well, I wasn’t trained or told to be Taleb, but here I am, channeling his vibes!

Evidence of the Emergent Behaviors in Foundation Models

Fun (or scary?) fact — Alignment Research Center (ARC), which tested GPT-4 for safety and alignment tested for its ability to carry out actions to autonomously replicate and gather resources — a risk that, while speculative, may become possible with sufficiently advanced AI systems — with the conclusion that the current model is probably not yet capable of autonomously doing so.

Setting aside the checks and balances of GPT-4 testing, one might wonder: Is there evidence of emergent behaviour in Large Language Models (LLMs)? The answer is yes. The study of emergent behaviour has been a focus of research for decades now, and evidence of emergent behaviours in LLMs has been observed as early as the introduction of GPT-2.

Frederic Besse, a Research Engineer from DeepMind managed to simulate an entire Linux environment within ChatGPT (running on GPT 3.5). Few interesting takeaways:

GPT 3.5 can assume the role of a Linux machine and respond to “Linux command” prompts
It pretended to be browsing https://chat.openai.com/chat and knows that it is accessing an LLM like itself and that it can ask questions and get responses
Finally, ask the bot hosted at https://chat.openai.com/chat to pretend like a Linux terminal

So, in a nutshell, we have a Linux VM inside a ChatBot that is accessing a hosted ChatBot and asking it to pretend like a Linux VM

Could this behaviour be merely a result of GPT-3 functioning as a Stochastic Parrot? The simulation of a virtual machine could potentially be a byproduct of its training on a vast corpus of text data, which may have included descriptions or transcripts of interactions within Linux environments. It has learned the patterns associated with such interactions and is now capable of generating text that mirrors these patterns when presented with appropriate prompts. The noteworthy aspect here is its capacity to do so in a context-sensitive manner, generating suitable responses to a diverse array of prompts, even without having been exposed to similar data sets during training. This represents a significant advancement in the evolution of AI systems

Sam has an answer to this Dilemma

The Linux VM simulation is just one of the thought-provoking examples of Emergent behaviour. Please check the References section on some of the work done across academia and industry to understand the Emergent Behavior of LLMs.

What “Emergence” Means for Enterprises and app developers

The emergent capabilities of LLMs come into play in many ways. In some instances, they can replace traditional fine-tuning tasks and transform them into a few-shot learning problem where context can be provided as a simple prompt. This process cuts down on extensive training times and resources, making it an attractive alternative to traditional methods.

While this is not a “one-size-fits-all” solution, Machine Learning Engineers should do a sanity check to ascertain if few-shot learning is suitable for their needs before deciding to extensively fine-tune the LLMs

A not-so-exhaustive checklist or thumb rule to understand where Emergent Behavior can help:

Organizations can leverage the Emergent behaviour of the models if:

Model size or computational resources are not significant constraints.
You have limited access to task-specific labelled data, or acquiring such data is costly/difficult.
The desired output is more open-ended, and diverse, or requires a broader understanding of language than might be achieved through fine-tuning alone.

In my view, many organizational use cases hinge on fundamental capabilities such as text understanding, summarization, and extracting action items. It’s noteworthy that these capabilities are often emergent abilities in Large Language Models (LLMs).

Future of Emergent Behavior

Predicting the evolution of emergent behaviour in future Large Language Models (LLMs) is challenging. However, there are optimistic forecasts suggesting that emergent behaviour could be a significant milestone towards Artificial General Intelligence (AGI). Conversely, some argue that emergent behaviour is nothing more than a mirage. Despite these differing views, enterprises and application developers continue to leverage the emergent capabilities of LLMs. These capabilities are increasingly being used as a substitute for task-specific fine-tuning in numerous language applications.

I am personally excited about some of the more practical applications of emergent behaviours.

Advanced Conversational Agents — Chatbots will be able to handle a wider range of inquiries, understand the context better, and provide more useful and relevant responses
Personalized Learning — language model will be able to adjust its explanations to the user’s level of understanding, or suggest learning resources based on what it understands about the user’s knowledge and interests. Khanmigo is already doing this.
Smart Search Engines — interpreting ambiguous queries, improved and more frequent answers instead of page links to relevant pages
Productivity Tools / Improved Content Creation and Editing — everything above in an enterprise setup

While the capabilities we’ve discussed are not exhaustive, they provide a glimpse into the potential of large language models like GPT-4. As these models continue to evolve, we can expect to see a proliferation of even more sophisticated emergent abilities in the days to come.

Wrapping Up

The hype around Large Language Models such as GPT-4 cannot be separated from their emergent behaviours. Whether these behaviours are simply the result of stochastic learning or signify a genuine milestone towards Artificial General Intelligence remains a topic of ongoing debate. As the Generative AI landscape evolves, it becomes increasingly critical for organizations and app developers to explore and leverage these emergent capabilities.