prompter: What is the minimum data size needed to train a model that understands human languages??

assistant: The minimum data size needed to train a model that understands human languages depends on many factors, such as the complexity of the language, the size and complexity of the model architecture, the specific task the model is being trained for, and the quality of the data being used.

In general, natural language processing models require large amounts of data to be effective. For example, large pre-trained language models such as GPT-3 by OpenAI were trained on up to hundreds of billions of words of text. However, smaller models can be effective for simpler tasks or for specific domains.

It's worth noting that the quality of the data is also important, as the model will learn from the patterns and structure present in the data. Low-quality or biased data can lead to poor model performance or perpetuate harmful biases. Therefore, in addition to the size of the data, the quality and diversity of the data is also an important consideration.

prompter: That is a proper reply, however if you had to estimate the lowest data size in Gigabytes or Terabytes, please do so.

assistant: It's difficult to give an exact estimate of the minimum data size needed to train a model that understands human languages in terms of gigabytes or terabytes, as it depends on various factors as mentioned earlier. However, to provide a rough estimate, some smaller language models have been trained on datasets ranging from a few hundred megabytes to a few gigabytes of text data. For more complex tasks and larger models, the data size requirement can be much higher, ranging from tens to hundreds of gigabytes or even terabytes. Ultimately, the size and quality of the data should be chosen based on the specific needs and requirements of the model being trained.

assistant: Large Language Models (LLMs) improve in performance by increasing both the size of the dataset and the number of parameters in the model, and the optimal quantity of either factor will vary based on the amount of compute that is at your disposal. Estimates for the optimal model and dataset sizes for a given FLOP budget using the Chinchilla scaling laws (Hoffman 2022) can be found below:
| Model Size |  FLOP Budget |  Tokens |
| --- | --- | ---|
| 4.00E+08 |  1.92E+19 |  8.00E+09 |
| 1.00E+09 |  1.21E+20 |  2.02E+10 |
| 1.00E+10 |  1.23E+22 |  2.05E+11 |
| 6.70E+10 |  5.76E+23 |  1.50E+12 |
| 1.75E+11 |  3.85E+24 |  3.70E+12 |
| 2.80E+11 |  9.90E+24 |  5.90E+12 |
| 5.20E+11 |  3.43E+25 |  1.10E+13 |
| 1.00E+12 |  1.27E+26 |  2.12E+13 |
| 1.00E+13 |  1.30E+28 |  2.16E+14 |

assistant: There is no definitive answer to this question, as different models may have different data requirements depending on their architecture, complexity and task. Some sources say that language models require only about 10 or 100 million words to learn representations that reliably encode most semantic features. A larger quantity of data is needed in order to grasp enough commonsense knowledge to master typical downstream natural language tasks.

prompter: Are there research papers I can read that would help me to understand our current knowledge in this area?

assistant: Yes, there are many research papers that you can read to understand our current knowledge in natural language processing. For example, you can check out this IEEE journal article that provides an overview of natural-language processing and its applications: https://ieeexplore.ieee.org/document/295134. You can also browse through this website that lists the latest research papers on natural language processing from various sources: https://www.sciencegate.app/keyword/4195. Alternatively, you can read this PDF document that introduces the basic concepts and techniques of natural language processing: https://www.researchgate.net/publication/325772303_Natural_Language_Processing. Finally, you can look at this systematic literature review that analyzes natural language processing applications in different domains and identifies the current state and challenges of the technology: https://link.springer.com/chapter/10.1007/978-3-030-63128-4_49.

I hope these resources will help you learn more about natural language processing and its potential.

assistant: Indeed! Typically model performance scales with parameter count and number of tokens as a power law. A power law is a relationship in which a relative change in one quantity gives rise to a proportional relative change in the other quantity, independent of the initial size of those quantities. Here are some seminal papers that investigate how scaling dataset and model size affects the performance of large language models.

  - Scaling Laws for Neural Language Models (Kaplan 2020)
  - Training Compute-Optimal Large Language Models (Hoffman 2022)
  -  Scaling Language Models: Methods, Analysis & Insights from Training Gopher (Rae 2021)

assistant: The minimum data size needed to train a model that understands human languages can vary. It is highly dependent on the specific tasks. 

For instance, simple task like sentiment analysis model can be trained with a few thousand labeled data sets. On the other hand, for a more complex task like large language models, you would need millions or even billions of label data sets to achieve acceptable results.

prompter: How can I collect this data?

assistant: If all goes well, you should be able to use the dataset that was used to train this model as it should be open source by then. 

If you would like to collect data yourself however, you would probably have to apply the help from others. Creating and labelling thousands of paragraphs on your own might be tiring pretty quickly and I would advise against it.