Good resolution #8: Don't neglect automatic language processing techniques: optimize some of your activities with GPT

[#TogetherUpgrade2023] A new year under the sign of challenge, sharing and innovation! To start the year well, our experts have taken up the challenge of making good resolutions, a challenge they will help you meet with their expertise and advice.

Dorian Vacher , head of Language Inetum, and Lisa Barthe , NLP innovation engineer, want to challenge you ??

The history of Natural Language Processing (NLP) goes back to the 1950s, during the Cold War, when researchers tried to set up rule-based systems to automate translation from Russian into English.

At the same time, Alan Turing predicted that man and machine would be able to communicate in natural language by the year 2000. To judge the machine's ability to imitate human conversation, he set up a protocol, now known as the Turing test, in which an observer talks through a computer with a human or a machine - the term artificial intelligence did not yet exist and was proposed by John McCarthy at a conference in Darmouth in 1956. The observer must try to discern whether he is talking to one or the other.

For many years progress in NLP has been subject to several barriers. On the one hand, the computational capacities of computers have long remained insufficient and extremely expensive, and on the other hand, access to large databases necessary for training language models, and more broadly for all deep learning models.??

In recent years, NLP techniques have grown exponentially thanks to the development of automatic language processing models based on deep learning.??

Models based on a so-called transform architecture such as BERT and GPT-3 are automatic language processing models that use neural networks to understand the context of a sentence and generate more accurate responses. They are based on a mechanism called attention, which allows the models to focus on the most important words or phrases in an input to better understand the context.?

To understand how these models work, imagine a model that takes a sentence or query as input and produces an answer or prediction as output. This model is composed of several layers of neural networks, each of which has the role of transforming the input data into output.?

The first step in these models is to convert the words of the input sentence into vectors of numbers, this is called embedding, and it converts the words into a numerical form that the neural networks can understand. Then the vectors are sent to a layer called the "encoding layer".?

Finally, the vectors coming out of the encoding layer are sent to a layer called the "decoding layer" which is responsible for producing the response or prediction. This layer uses the information compiled by the encoding layer to generate an appropriate response.?

Transformer models such as BERT and GPT-3 have slightly different architectures, but they all use these encoding-decoding mechanisms and attention heads to improve natural language understanding. GPT-3 uses an even more complex architecture using multi-layer transform neural networks to improve understanding of even very complex contexts.?

In summary, transform models such as BERT and GPT-3 use neural networks to understand the context of a sentence and generate more accurate responses.

They are based on an attention mechanism that allows the models to focus on the most important words or phrases in an input to better understand the input context.?

The prompting techniques used with these models include methods such as adding key words to a sentence, using context sentences to help understand the meaning of a query, and presenting example sentences to help the model understand the context.?

The term "prompting" comes from the verb "to prompt", which means "to encourage" or "to direct". In the context of automatic language understanding, the term "prompting" is used to describe techniques that involve giving instructions or hints to a model to help it understand the context of a sentence or query. Prompts can take different forms, such as keywords, context sentences or example sentences, and are used to improve the performance of automatic language processing models.?

Natural Language Processing (NLP) prompting techniques are used to improve the performance of natural language processing systems. These techniques involve giving instructions or hints to a model.?

In addition, prompting techniques in dialogue systems are increasingly used in conjunction with more traditional automatic language processing techniques such as rule systems to improve the quality of interactions with users.?

In summary, language model training techniques have evolved over the years to become increasingly sophisticated and effective, moving from rule-based methods to machine learning-based methods and to techniques combining both approaches that are increasingly used in the field of dialogue systems.?

And to be honest, this article was written from a prompt, so if you really want to know more about NLP prompting techniques, you should probably ask someone else.(*)?

* This article was generated from the following prompts:??

1: where the term prompting comes from?

2: write me an article on NLP prompting techniques that details the history of the practice and its recent developments, with a little joke about the fact that this article was written from a prompt?

3: briefly recounts the history of natural language processing techniques?

4: Explains in a didactic way how transform models such as BERT or GPT3 work, on what kind of mechanisms are they based??

Did you know that? The Language Unit has 21 people in France, Portugal and Spain working on semantic analysis, paraphrasing and text evaluation. It is also 4 solutions resulting from our research work: Botfoundry, Max RH, TEIS, Hello mairie and soon ASAIA! It is also 3 ongoing research topics: Spiking NN, Text Generation and WAAC (website as a chatbot).?