ChatGPT For NLG: Learn How This AI Is Revolutionising Technology?

The new age Artificial Intelligence-based technologies are capable of making the user experience seamless. One of the ways in which it is done is by using Natural Language Processing (NLP) and Natural Language Generation (NLG) technologies. While NLP has been in use for many years, NLG is not behind. OpenAI's ChatGPT supports NLG to offer responses that resemble human-like language. In this article, we will explore how ChatGPT for NLG is revolutionizing the world of AI technology.

What is NLG?

In the field of Artificial Intelligence, NLG is used for generating responses in human-like language. NLG is a subfield of computational linguistics in which a computer system is trained to understand text written in a human language. With the help of NLG, a machine can mimic the patterns of human language, and develop an ability to produce texts in similar ways.

Some of the uses of NLG include:

• Analytical reports
• Content generation
• Personalized customer responses
• Product descriptions
• Social media posts/captions

The NLP/NLG Difference in ChatGPT

ChatGPT is known for its Natural Language Processing (NLP) abilities. The chatbot operates on Large Language Models (LLMs), which enables it to work with a great number of human languages.

The NLP technology in ChatGPT is used for analyzing texts written in human-like language. In other words, it means that when a user inserts an effective prompt, the chatbot is capable of analyzing it. It breaks the linguistic components of a prompt and places each component into a different category.

For instance, if ChatGPT is given the following prompt,

The chatbot analyses the prompt for linguistic elements and tries to understand its demand. The workings of the chatbot can be understood through these phrase structure rules which are used in computation linguistics:

[[generate_VP][the summary_NP][of the following article_PP][in 300 words_PP]]

The chatbot is trained to break these phrase structure rules into even smaller parts. For instance, the preposition phrase "of the following article" is further broken as:

[[of_P][the_Det][following_Adj][article_N] _PP]

Here, we will see that the prepositional phrase is further analyzed for the essential components of linguistics. After the chatbot's NLP technology analyses the elements of the human language text, it establishes the semantic and pragmatic relationship between them. Hence, the chatbot will know that the average user will be asking for the 'summary' of a particular article in the desired number of words.

In this way, the NLP infrastructure of ChatGPT helps it analyze human language texts.

On the other hand, NLG allows it to create human texts which match the linguistic nuances of human language. For example, the chatbot can provide jokes and motivational messages for you. The interesting aspect of the NLG technology here is that the responses are created to sound just like a human is answering.

For example, we asked ChatGPT to tell a joke (the first image). We also asked the chatbot to provide us with some advice on dealing with exam stress (the second image). You can notice here, that the chatbot uses the first person pronoun, 'I' to establish familiarity with the regular user. Thus, it can be seen that ChatGPT for NLG allows the chatbot to generate and respond in a human-like language.

We hope now you have a clear understanding of the difference between NLP and NLG. This knowledge will allow you to comprehend how NLG makes ChatGPT different from another AI-based chatbots.

ChatGPT for NLG

ChatGPT is a reliable and powerful tool to perform natural language tasks. The chatbot possesses state-of-the-art infrastructure which makes it capable of a range of tasks in NLG, such as story generation, summarisation, and data-to-text tasks.

ChatGPT for NLG: Text Prediction

ChatGPT's NLG ability relies on how well it analyses the prompt or the given text. Like other LLM models, ChatGPT uses a training process for predicting the next token on the basis of the tokenized version of the input. Once a textual input is fed into the system of the chatbot, it assigns a probability to each token with the help of its large corpus of tokens.

ChatGPT for NLG utilizes a decoding procedure to complete the input text by calling the advanced language model several times. Like this, the chatbot determines the possible continuation or word which should appear in the given text. This is how the chatbot conducts text prediction.

ChatGPT for NLG: Adding emotions

After the process of text prediction is done, the real function of NLG is introduced. The job of the NLG technology is to make the AI-generated text look like it is produced by a human. For this, the generated text should have certain key factors in place. These include the right tone of voice, appropriate structure, and the display of an adequate amount of emotions. In other words, it should be able to replicate human thinking for generating the correct reply.

Here, ChatGPT for NLG is assisted by a supervised learning model of Machine Learning. In this model, machines are guided by manual chatbot trainers. Their role is to train the chatbot to improve its conversational abilities.

In supervised learning, also known as Reinforcement Learning from Human Feedback (RLHF), raw data is organized into algorithms. These algorithms allow the chatbot to predict the outcomes of unforeseen and new input.

The interesting aspect of the RLHF method is that it operates on a reward model. It means that the AI-based chatbot is offered a high reward when it gives the right response. On the other hand, the chatbot is offered a low reward when it produces an incorrect response.

Here, the role of the chatbot trainers is to determine whether the response created by ChatGPT is correct or not. In other words, they evaluate whether the chatbot is able to process the emotional tone of the text. Based on the response, the chatbot is given either a high reward or a low reward by the trainers. Through its Machine Learning, then, AI-based technology learns to produce an accurate response with an appropriate emotional tone.

This feature of ChatGPT for NLG is an ideal choice for industries where professionals have to regularly interact with people. For example, in the customer care industry, it is important to train chatbots and virtual assistants to use emotive language.

ChatGPT for NLG: Data Extraction

ChatGPT for NLG uses already existing data to form human-like responses. For this purpose, the chatbot uses Convolutions Neural Networks (CNN). The CNN model has the capability of producing relevant responses from raw data.

Here, the presence of chatbot trainers is crucial. The chatbot does the work of forming a response from the extracted data and the chatbot trainers determine whether the response is useful or not.

NLG for Information Creation and Sharing

ChatGPT is an ideal tool for professionals as it is changing the way ecosystem information is processed and shared. In the world of NLP and NLG, ecosystem information is understood as data and knowledge of ecosystems which can assist in generating human-readable texts. The process of ecosystem information requires incorporating characteristics, relationships, and facts into the generated text. This information helps the NLG system to generate descriptions, narratives, and reports about the elements and functioning of the ecosystem. The chatbot is designed to support ecosystem information that is up-to-date, accurate, and comprehensive.

Along with this, ChatGPT is also trained to use algorithms and generate human-like language from the available data. For instance, the chatbot can create a report on a particular ecosystem, say, a specific region. ChatGPT can conduct research on a given topic, analyzing different aspects of it. Later, this information can be used to create a report on that particular ecosystem.

The ecosystem information report produced by ChatGPT has a range of applications in various industries. It can be of great help in the business processes and finance sector, where a wide range of decisions are based on ecosystem information. Additionally, the healthcare, educational, and marketing sectors also benefit from this.

Benefits of ChatGPT for NLG

Now that we understand how ChatGPT for NLG works, let's explore some of its benefits.

Generating human-readable texts: The chatbot applications include a capacity for high-quality content creation which is easily readable by people such as news stories, summaries, and reports.

Automating the process of creating reports: NLG can help organizations save money and time in generating reports by automating the process.

Generating personalized reports: NLG may be used to provide customized reports for stakeholders, including public entities, investors, and government organizations. In this way, NLG can make sure that important stakeholders are able to access information and generate personalized reports.

Additionally, NLG can aid organizations in more efficient information sharing and reporting on ecosystems. Organizations may provide customized reports for their stakeholders by automating the process. This may assist businesses in better engaging their stakeholders and ensuring that they comprehend the information and can utilize it to make wise decisions.

Saving money: Organisations can save money with NLG. By automating the process of generating reports, a vast amount of cost can be saved. This can lower overhead expenses for businesses and free up resources for other areas of their operations.

So, these were the uses and benefits of ChatGPT for NLG. The generative AI technology of ChatGPT, with its conversational abilities, is equipped to not only analyze but generate responses in human-like language. In this way, the intelligent chatbot offers enhanced AI services for the users.

We hope this article was useful and insightful. We have brought a wide range of articles on ChatGPT. These will keep you updated with the latest technological developments so that you can grab various opportunities.

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