Can you explain, in details how you work, and include scientific reference ?
Sure! Open Assistant is an Open-Source project meant to give everyone access to a great chat based large language model.
A language model (LM) is an AI model that is trained to predict the probability of a sequence of words in a given language. A language model can be trained in two ways: either as a Generative model, which can generate new text, or as a Discriminative model, which can rank the likelihood of a given text being correct.

A large language model (LLM), uses a deep neural network with a Transformer architecture. The model is trained on a massive corpus of text data, where it learns to predict the next word in a sequence given the previous words. The LLM is then fine-tuned for specific NLP tasks, such as text classification, question answering, and machine translation, by training it on task-specific data.
(Scientific Reference: https://research.google/pubs/pub46201/)

LLMs have proven to be highly effective in various NLP tasks due to their ability to capture and leverage large-scale patterns in language, often outperforming traditional rule-based systems and other machine learning models.

*Note - You can learn more details about the Open Assistant project here: https://docs.google.com/presentation/d/1iaX_nxasVWlvPiSNs0cllR9L_1neZq0RJxd6MFEalUY
Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:

Can you explain, in details how you work, and include scientific reference ?

### Response:

Sure! Open Assistant is an Open-Source project meant to give everyone access to a great chat based large language model.
A language model (LM) is an AI model that is trained to predict the probability of a sequence of words in a given language. A language model can be trained in two ways: either as a Generative model, which can generate new text, or as a Discriminative model, which can rank the likelihood of a given text being correct.

A large language model (LLM), uses a deep neural network with a Transformer architecture. The model is trained on a massive corpus of text data, where it learns to predict the next word in a sequence given the previous words. The LLM is then fine-tuned for specific NLP tasks, such as text classification, question answering, and machine translation, by training it on task-specific data.
(Scientific Reference: https://research.google/pubs/pub46201/)

LLMs have proven to be highly effective in various NLP tasks due to their ability to capture and leverage large-scale patterns in language, often outperforming traditional rule-based systems and other machine learning models.

*Note - You can learn more details about the Open Assistant project here: https://docs.google.com/presentation/d/1iaX_nxasVWlvPiSNs0cllR9L_1neZq0RJxd6MFEalUY
