Large language models (LLM) are being utilized across a wide variety of use cases, from small-scale entrepreneurs using them to increase their productivity, through to industry-leading conglomerates condensing swathes of data into manageable insights.

We will delve into the following four use cases of LLMs that have positively impacted a wide range of industries:

• Text Generation
• Translation
• Summarizing Big Data
• AI Chatbots

Text Generation

It is hard to overstate the wave of changes that generative tools such as ChatGPT have brought about.

In November 2023, the platform hit 100 million weekly users, a number that continues to grow.

It is easy to understand the appeal of text generation platforms. Users can provide a written prompt, and within seconds receive an answer in whatever depth and level of expertise they require.

Shortly after its launch, educators were warning about the impact that ChatGPT was having on essay writing and homework. A Forbes report found that up to 89% of students admitted to using ChatGPT to some extent within their school work.

ChatGPT got so adept at writing academic papers that Wharton MBA Professor Christian Terwisch claimed it would likely get “a B or a B-” on an Ivy League MBA-level exam. Professor Alex Lawrence declared it “the greatest cheating tool ever invented”.

The first generation of ChatGPT, GPT-1, was an LLM trained on 11,000 unpublished books. The purpose of this early model was limited to language understanding.

GPT-2 had a far wider scope of use cases, and was marketed as being “too dangerous to release”. It was trained on over 40 billion tokens of text. This text was taken from web pages linked to from within over 8 million reddit articles. A later iteration of GPT-2 was deployed to generate musical compositions with a number of different instruments.

GPT-3 was released in June 2020, having been trained on over 175 billion parameters. This was the first ChatGPT iteration that had the ability to comprehend and write human-like text. It was this generation that started the text generation revolution. GPT-3 could be used for a variety of language tasks, such as translation, summarizing and question answering, although it was criticized for a lack of common sense and an inherent bias.

GPT-4 was released in May 2024, and brings about real time multimodal understanding. It can reason audio, vision and text simultaneously, as well as having far quicker response times than the previous generations.

Translation

LLMs represent the biggest breakthrough to translation since the Rosetta Stone was discovered to hold the secrets to deciphering Egyptian scripts.

LLMs excel in translation, especially models trained to understand cultural nuances. For example, a literal translation of “it’s raining cats and dogs” into French wouldn’t make much sense to a native French speaker.

Instead, a trained LLM model would produce a translation of “Il pleut des cordes”, the equivalent French saying, rather than the literal translation. This contextual understanding and ability for idiomatic translation allows for translations that sound natural.

Key features of LLM for translation purposes:

• Contextual understanding of the ambiguities in speech to provide accurate translations
• Multilingual capabilities
• Idiomatic translations to understand the nuances between different languages and expressions

Summarizing big data

LLMs can be trained to help summarize large data into concise summaries. These models can spot patterns and key insights with efficiency and accuracy.

They can be trained to provide two main summarisation tasks, namely:

Extractive Summarization

An LLM trained to perform extractive summarization will select key phrases, sentences and data from large datasets

Abstractive Summarization

These LLMs will contextually understand the data, and then generate summaries

Uses of data summarization LLMs

• Business intelligence purposes such as market research and customer feedback analysis
• Healthcare purposes such as summarizing patient records and clinical trial data
• Summarizing legal contracts and documents into the key clauses to review
• Use within the scientific community to simplify vast datasets or research papers

Challenges of training an LLM for data summarization

If the LLM is intended to be able to summarize large datasets, then the original training process will require extensive data sources and computational power.

This becomes especially challenging if the large datasets that will need summarizing are complex or specialist in nature as opposed to general passages of text.

Finding large quantities of relevant data without inherent biases that might skew the summarization tool is difficult for organizations of all sizes.

AI Chatbots

Digital chatbots can help to improve the efficiency of a website’s customer service, providing round-the-clock support and communication to visitors.

Whilst early iterations of chatbots were cumbersome and too vague to be of much use, the latest generation of AI-powered custom chatbots are helping to provide accurate responses that help customers with their queries.

This is achieved by training the LLM to understand what the customer is likely to ask, as well as providing the necessary responses.

This pre-learned ability allows the chatbot to answer the query quickly and accurately. Should the customer ask a question that the chatbot has not been trained to understand, this can be easily escalated to a human support agent to handle the enquiry.

Introducing the NetGeist Chatbots

NetGeist trains and develops custom chatbots for your organization. Our chatbots are trained on large datasets that can be added to over time, helping your custom chatbot to continue learning and improving.

We can provide custom chatbot solutions powered by AI for any industry and use case. Whether you require a particular communication style, have a very specific dataset or need a custom integration into your website, software, app or infrastructure, we can create a smooth user experience that will improve your workflow.

We have two example chatbots to check out. Our financial chatbot on StockGeist.ai can answer questions about cryptocurrencies, terminologies and much more. Assistant Robert has been trained to provide specific answers regarding Neurotechnology’s range of products and services.

For a custom quote about a chatbot for your organization, contact us.