AI, Natural Language & Generative Models

AI & Generative models
AI & Generative models

Natural language processing (NLP) is the branch of artificial intelligence (AI) that enables machines to understand and interpret human language. It allows us to interact with technology in a more intuitive way, making it possible for us to converse with chatbots, virtual assistants, and smart speakers like Alexa or Siri. In this article, we will explore how natural language works and how generative models produce the right output based on the user’s query.

How does NLP work?

The natural language processing (NLP) pipeline consists of several steps. The first step is tokenisation, which involves breaking down a sentence into individual words or tokens. This step is necessary because computers understand language at the most granular level, i.e., individual words. Once the sentence has been tokenised, the next step is parsing, which involves analysing the grammatical structure of the sentence. This is where the computer identifies the subject, verb, and object of the sentence, as well as any adjectives, adverbs, or other modifiers.

The next step is semantic analysis, which involves understanding the meaning of the sentence. This is where the computer identifies any named entities, such as people, places, or organisations, and their relationships to each other. It also involves analysing the context of the sentence to determine the intent behind the user’s query.

Finally, the last step is generating a response, which involves selecting the appropriate response based on the user’s query and generating a natural language response that can be understood by the user.

How can Generative Models support NLP?

Generative models are a type of artificial intelligence algorithm that can be trained to generate natural language responses. These models work by analysing large datasets of human language to identify patterns and relationships between words and phrases. The most commonly used generative model in NLP is the neural language model. A neural language model is a type of artificial neural network that is specifically designed to analyse and generate natural language. The network is trained on a large dataset of human language, such as a corpus of news articles or social media posts. The network learns to identify patterns and relationships between words and phrases, and uses this knowledge to generate natural language responses to user queries. To generate a response, the neural language model first receives the user’s query as input. The model then analyses the query using the natural language processing pipeline we discussed earlier. Once the query has been tokenised, parsed, and semantically analysed, the model selects the appropriate response from its database of pre-written responses. However, the response generated by the neural language model is not simply a pre-written response from a database. Instead, the model generates a response on the fly based on the user’s query and the patterns it has learned from the training data.

For example, if a user asks, “What’s the weather like today?” the neural language model might generate a response like, “It looks like it’s going to be sunny and warm today.” The model generates this response based on the patterns it has learned from the training data, which indicate that users asking about the weather typically want to know about current conditions and the forecast for the day. In some cases, the neural language model may not have a pre-written response that exactly matches the user’s query. In these cases, the model may generate a response by combining or modifying existing responses to create a new response that is more relevant to the user’s query.

For example, if a user asks, “What’s the best Italian restaurant in town?” the neural language model may not have a pre-written response that exactly matches the query. However, it may be able to generate a response by combining information from several pre-written responses, such as “There are several great Italian restaurants in town, including Pizzeria Uno, Bella Italia, and Luigi’s,” and “Many people consider Pizzeria Uno to be the best Italian restaurant in town.”

While generative models like neural language models have made significant strides in improving the accuracy of natural language processing, there are still limitations to their abilities. One of the biggest challenges is the issue of context. Natural language is incredibly nuanced, and the meaning of a sentence can change depending on the context in which it is used.

For example, the sentence “I saw her duck” could mean that you saw someone ducking down or that you saw a duck that belongs to her. The meaning of the sentence is dependent on the context in which it is used. To address this challenge, some NLP models incorporate contextual information by using a technique called contextual word embeddings. Contextual word embeddings are a type of word representation that captures the meaning of a word based on its surrounding words. One popular contextual word embedding model is called BERT (Bidirectional Encoder Representations from Transformers). BERT is a deep learning model that is trained on a large corpus of text and can be used to generate contextual embeddings for any given sentence. By using contextual embeddings, NLP models like BERT are able to capture the nuanced meaning of natural language and generate more accurate responses to user queries.

In conclusion, natural language processing is a complex and rapidly evolving field that is enabling machines to understand and interpret human language. Generative models like neural language models are a key part of this field, as they are able to generate natural language responses to user queries based on patterns and relationships learned from large datasets of human language. While generative models have made significant progress in improving the accuracy of natural language processing, there are still challenges to be addressed, such as the issue of context. However, with the continued development of techniques like contextual word embeddings and the growing availability of high-quality training data, it is likely that we will see even greater advancements in the field of natural language processing in the years to come.

If you feel your organisation may benefit from Natural Language powered Business Intelligence and would like to gain a more in depth understanding, please do get in touch via email on betintel[@]aretonet.com.

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