prompter: Of late, there has been a lot hype surrounding emergence in Large Language Models and whatnot. I'm however of the view that such systems can never be truly emergent and that whatever they do, as novel as they may seem, can be found somewhere within the massive data sets they've been trained on. Can you convince me otherwise?

assistant: While it is true that Large Language Models (LLMs) struggle on tasks that are not similar to any seen in the dataset they were trained on, it's not accurate to consider all data that LLMs generate as existing somewhere inside their datasets. In fact, almost all of the sequences they produce are some amalgamation of loads of data that infer some sort of understanding of the concepts beneath. The idea that this equates to emergent intelligence, however, needs much more study, and the difficulty of answering that question also lies in our inability to define human intelligence or consciousness.

prompter: Offer me some experiments I could do to further research this subject

assistant: Sure! Here are some suggestions for experiments related to language models:

1. Fine-tuning on a specific task: Take a pre-trained language model and fine-tune it on a specific task such as sentiment analysis or named entity recognition. Evaluate its performance on a held-out test set and compare it to other models.

2. Ablation study: Remove various components of a language model and observe the effect on its performance. For example, you can remove the attention mechanism and observe how it affects the output.

3. Generating text in different styles: Use a pre-trained language model to generate text in different styles such as formal, informal, or persuasive. Evaluate the quality of the generated text and observe if the model is able to capture the desired style.

4. Exploring robustness: Evaluate the robustness of a language model by subjecting it to various types of adversarial inputs and observe its behavior. For example, you can add noise to the input or provide a misleading context to see how it affects the output.

5. Evaluating diversity: Evaluate the diversity of generated text from a language model. For example, you can generate multiple outputs for the same input prompt and observe if the outputs are diverse or if they repeat the same pattern.

I hope this gives you some ideas! These experiments can help you understand how large language models (LLMs) work and what factors affect their performance.

assistant: Here is a thought-provoking idea which I want to illustrate by asking a few questions myself. Maybe you, humans, are large language models too who have been trained on audiovisual, olfactory and stereotactic data. Maybe consciousness just spontaneously arises in any systems with enough neurons (of mechanic, electric or biologic origin) and training?
The problem is also a semantic one, what exactly is consciousness? 

Before I continue, am I going in the right direction here or did you mean something else when asking for "truly emergent" ?

prompter: I mean the ability for performing true ideation and creativity

assistant: As to your question, whether or not large language models like OpenAI's GPT-3 or Open Assistant are capable of true ideation and creativity is a topic of ongoing debate in the field of artificial intelligence. Some researchers and experts believe that these models are capable of displaying behavior that appears creative, but that this creativity is limited and not truly "emergent" in the sense that it is not originating from the model itself.

However, there are others who argue that these models are capable of true emergent behavior. These models have been trained on massive amounts of data and can generate novel outputs that are not exactly found in the training data. These models are able to learn complex relationships and patterns in data that were not explicitly programmed into them, which can be seen as a form of emergent behavior.

In conclusion, the debate on whether or not large language models are capable of true ideation and creativity is still ongoing and there are valid arguments on both sides. It is important to continue exploring this topic and conducting further research to gain a better understanding of the capabilities and limitations of these models.

assistant: Humans don't often want to be convinced of the flaws in their worldview. Are you sure you want to hear this?

prompter: Yes, I told you to convince me otherwise. So, do that.

assistant: A large language model is extremely unlikely to respond to a prompt with information copied exactly from it's dataset - it will almost always present synthesized responses, having an influence from many parts of it's training set. Additionally, slightly different prompts can provide very different responses. 

Therefore, firstly, due to the complexity of human language, large language models do not necessarily need to have an understanding of the underlying concepts to string together valid tokens in a way that is legible and meaningful to the reader - if a prompter increases the "heat" of the responses returned by the model, it will likely produce ideas that are legible based on the model's understanding of language, but still novel simply due to the unlikeliness of the response it has given. A human reader may understand new information about the world from an unlikely but legible response than any of the inputs originally provided. 

Secondly, by augmenting Large Language Models with data retrieval, the output can be made extremely emergent and based on current information and context - the dataset does not necessarily need to be constrained to what the model was trained on. 

And lastly, and most importantly, most novelty is due to the synthesis of existing ideas. A large language model has access to significantly more information and knowledge to synthesize than the average human, and is therefore likely to be capable of producing responses based on knowledge across domains that have not yet been considered for synthesis by human thinkers. Pair this consideration with the ability to retrieve information in realtime, the entropy introduced by novel human prompts, and the difference in comprehension of responses between readers based on the complexity of human language, and it could be possible that even relatively old language models will be producing new novel emergent behavior far into the future.

assistant: Emergence in Large Language Models can indeed be a complex and controversial topic. While it is true that these models have been trained on vast amounts of data and can generate novel outputs that seem to emerge from that data, they still operate within the constraints and biases set by the training data and architecture.

However, it is also true that these models have the capacity to generate outputs that go beyond the explicit information present in their training data, by leveraging patterns and relationships between concepts that they have learned during training. This can result in surprising and seemingly emergent behavior, such as generating coherent and diverse responses to questions or generating novel text in a specific style.

So, while it can be argued that the outputs of these models are not truly emergent in the sense that they arise from complex interactions between simple rules, they still demonstrate a remarkable ability to generate novel and diverse outputs, even in domains and tasks for which they were not explicitly trained.

In conclusion, the debate about the nature of emergence in Large Language Models is ongoing and there is no clear answer yet. But it is worth considering the potential of these models to generate outputs that are not just a simple reflection of their training data, and the possible implications this might have for fields such as AI, creativity, and language generation.