ChatGPT vs. other language models
A model in machine learning is a file that is trained over a set of data to generate required outputs. For example, some models are used to help predict future outcomes based on past data, such as the models used in weather forecasting.
Language models are models trained to process human language. These models are used in various applications such as chatbots, translation tools, content classifiers, etc. Some examples of language models are given below:
ChatGPT
LaMDA
BERT
RoBERTa
ELMo
ULMFiT
ChatGPT vs. other language models
Let's compare ChatGPT with other language models by putting them into different categories and use cases.
Architecture
The architecture of models describes how the underlying neural network is arranged to process the input and generate an output. Details about the architecture used in different language models are given as follows:
ChatGPT: It is based on the Generative Pre-trained Transformer (GPT), which is a transformer-based neural network.
LaMDA: It is based on the transformer architecture.
BERT: Bidirectional Encoder Representations from Transformers (BERT) is based on the transformer architecture.
ELMo: It is based upon bidirectional RNN architecture consisting of two bidirectional units.
ULMFiT: It uses a combination of a Long Short-Term Memory (LTSM) network-based language model and a fine-tuning approach.
Training data
Training data is the data over which we train our model to generate the required response. Let's see the training data used for different language models below:
ChatGPT: ChatGPT uses different sources as training data, such as web pages, social media posts, and online forums. Some of the datasets it uses include:
Reddit comments
The OpenWebText dataset
The BookCorpus dataset
LaMDA: It uses different sources as training data, such as web pages, social media posts, and online forums. The training data was created by combining web crawling and data scraping techniques.
BERT: BERT uses different sources, including books, web pages, and Wikipedia. Some of the datasets it uses include:
The BooksCorpus dataset
The OpenWebText dataset
The New York Times dataset
ELMo: The original version of ELMo was trained in a dataset of 5.5 billion words. This dataset includes a combination of publicly available data and in-house data.
ULMFiT: Some of the sources used to create training data for ULMFiT are as follows:
Wikipedia
The BookCorpus dataset
The OpenWebText dataset
Common Crawl
Use case
Different language models have different use cases. Let's look into the use cases of some language models:
ChatGPT: It is used in chatbots to generate human-like responses to different queries.
LaMDA: It can be used for translations, text classification, and sentiment analysis. It can also be used to develop chatbots, virtual assistants, etc.
BERT: It is mainly used for question answering.
ELMo: It is used to find the contextual meaning of phrases. Some of its applications include speech recognition, language translation, and sentiment analysis.
ULMFiT: It is used for text classification and sentiment analysis.
To sum up, ChatGPT and other language models differ in terms of their architecture, training data sets, use cases, etc. Each of them has its own strengths and weaknesses. We can select any language model based on our requirements.
