Large Language Models: Definition, Application Areas, Future Trends

Understanding LLM Models 

What is LLM model? Large Language Models (LLMs) are networks based on machine learning techniques and can answer queries in human language with human language using billions of pieces of data. They provide the basis for AI chatbots like Bard or GPT models, which process natural language input and predict the next word based on historical data. Simply put, LLMs could be described as «text prediction machines.»

LLMs use neural network-based models and often use natural language processing (NLP) techniques to process and calculate their results. NLP is a field of artificial intelligence that deals with the ability of computers to understand, interpret and generate text. This, in turn, enables LLMs to perform the following tasks:

1. Text analysis: LLMs use text analysis to extract insights from large volumes of unstructured text data, such as analyzing customer feedback and conducting opinion mining to identify patterns in product satisfaction.
2. Sentiment analysis: LLMs employ sentiment analysis to determine the emotional tone of a piece of text, enabling businesses to gauge customer opinions and sentiment towards their products or services.
3. Language translation: LLMs facilitate language translation by accurately converting text from one language to another, allowing users to communicate across language barriers in real time.
4. Speech recognition: LLMs utilize speech recognition technology to transcribe spoken language into text, enabling applications like virtual assistants to understand and respond to spoken commands and queries.

LLMs can be used for content curation based on human language prompts. They can take billions of parameters into account and have many possible uses, and multiple LLM use cases show the technology’s different strengths and weaknesses. When reviewing benefits of large language models, evaluating their specific disadvantages as well is crucial based on your business needs and use cases. As a company with a comprehensive range of data science services, Lightpoint can help you choose the language model that best fits your desired outcomes and delivers the greatest potential.

How Are LLMs Trained?

The number of parameters combined with the transformer model allows LLMs to quickly generate accurate answers, allowing AI technology to be used in various areas. But before a large language model can receive text input and generate an output prediction, it requires training to enable it to perform general functions and fine-tuning to enable it to perform specific tasks. An LLM needs to be trained on petabytes of data at the basic level, and here are the essential steps to start.

Step 1: Data collection

The first step involves collecting vast amounts of text data from various sources such as books, articles, websites, and other textual sources. This data serves as the foundation for training the LLM.

Step 2: Preprocessing

The collected text data undergoes preprocessing to clean and standardize the text. This may involve removing special characters, tokenizing the text into words or subwords, and converting the text into a format suitable for training.

Step 3: Training the base model

The preprocessed text data is used to train the base LLM model using unsupervised learning techniques such as self-supervised learning. During training, the LLM learns to predict the next word in a sequence given the preceding context. This process helps the model deeply understand language patterns and relationships.

Step 4: Fine-tuning

After the base model is trained, it undergoes fine-tuning to adapt it to perform specific tasks or domains. Fine-tuning involves providing the model with task-specific labeled data and adjusting its parameters to optimize performance on the target task. 

For example, if the LLM is being fine-tuned for sentiment analysis, it would be trained on a dataset of labeled sentiment data to learn how to classify text based on sentiment.

Step 5: Evaluation and iteration

Once fine-tuning is complete, the performance of the LLM is evaluated on a separate validation dataset to assess its accuracy and effectiveness. Based on the evaluation results, adjustments may be made to the model architecture, training process, or fine-tuning approach to improve performance.This iterative process of training, fine-tuning, evaluation, and adjustment continues until the desired level of performance is achieved in all LLM use cases. It involves collecting and preprocessing data, training the base model on general language understanding tasks, fine-tuning the model for specific tasks, and optimizing its performance through evaluation and adjustment.

Examples of Large Language Models in Action

Researchers are exploring large language models examples to better understand the capabilities and limitations of these advanced AI systems, as they’re constantly evolving. The most widely used today include:

1. GPT (Generative Pre-trained Transformer) models: GPT models, developed by OpenAI, are based on the Transformer architecture, which consists of encoder and decoder layers. They use a transformer decoder architecture to generate text and are pre-trained on vast amounts of text data using unsupervised learning. During pre-training, GPT models learn to predict the next word in a sequence given the previous context. This enables them to understand and generate coherent text based on input prompts.
2. BERT (Bidirectional Encoder Representations from Transformers): BERT, developed by Google, is designed to understand the bidirectional context of words in a sentence. It uses a transformer encoder architecture, contextualizing each word based on both left and right contexts. During pre-training, BERT models are trained on large text corpora using masked language modeling and next-sentence prediction tasks. This allows them to capture deep contextual relationships between words and phrases.
3. T5 (Text-To-Text Transfer Transformer): T5, developed by Google, is a unified framework for various natural language processing tasks, where all tasks are framed as text-to-text transformations. T5 models utilize the transformer architecture but differ from other models by treating all NLP tasks as text-to-text problems. During pre-training, T5 models are trained to map input text to output text for various tasks, including translation, summarization, question answering, and more.
4. XLNet (Transformer-XL Network): XLNet, developed by Google, introduces an autoregressive permutation language modeling approach. They use a transformer architecture similar to GPT but incorporate the idea of permutation language modeling, where tokens can be predicted in any order. During pre-training, XLNet models are trained to predict tokens in a sequence while considering all possible permutations of the input sequence, allowing them to capture bidirectional context more effectively.
5. Others (OpenAI Codex, CTRL, etc.): OpenAI Codex is a language model developed by OpenAI that is trained on a vast code and natural language text dataset. It is designed to understand and generate code based on natural language prompts. CTRL (Conditional Transformer Language Model) is developed by Salesforce Research and allows users to control the style and content of the generated text.

These models leverage large-scale pre-training on diverse datasets to understand language patterns and generate coherent text based on input prompts, making them versatile tools for various natural language processing tasks.

1. Natural Language Generation (NLG)

NLG can be employed to generate personalized product descriptions for e-commerce platforms based on user preferences and browsing history. For instance, an online retail giant can use NLG to create dynamic product descriptions tailored to individual customers, enhancing their shopping experience and increasing conversion rates.

2. Language translation and localization

Language translation and localization can aid global businesses in translating website content to multiple languages for international audiences. A multinational corporation might use language translation services to localize its marketing campaigns, ensuring that messages resonate with diverse cultural and linguistic audiences across different regions.

3. Question answering systems

Question-answering systems can assist customer support teams in addressing inquiries and troubleshooting issues more efficiently. A telecommunications company could deploy a question-answering system to respond instantly to frequently asked questions about service plans, billing inquiries, and technical support, thereby reducing customer wait times and improving satisfaction.

4. Text summarization

Text summarization can streamline the process of analyzing large volumes of research papers or legal documents. For instance, a legal firm might use text summarization algorithms to generate concise summaries of lengthy court cases or contracts, enabling lawyers to quickly extract key insights and make informed decisions.

5. Chatbots and virtual assistants

Chatbots and virtual assistants can enhance customer service experiences by providing 24/7 support and instant responses to user inquiries. An airline company could deploy a chatbot on its website to assist travelers with booking flights, checking flight statuses, and resolving common travel-related queries, improving overall customer satisfaction and retention.

6. Fraud detection and cybersecurity

Fraud detection and cybersecurity systems can analyze large datasets to identify suspicious activities and prevent fraudulent transactions. A financial institution could leverage AI-based tools to detect anomalous patterns in banking transactions and flag potentially fraudulent activities in real time, safeguarding customer assets and maintaining trust in the banking system.

7. Medical text analysis and diagnosis

Medical text analysis and diagnosis tools can assist healthcare professionals in interpreting medical records and making accurate diagnoses. For example, a hospital might use natural language processing algorithms to analyze electronic health records and identify patterns indicative of specific diseases or medical conditions, facilitating timely interventions and improving patient outcomes. Given the broad scope of application, many businesses leverage natural language processing as a service to harness the full potential of large language models. Integration with AI technologies allows them to extract valuable insights and enhance customer experience across diverse domains with unparalleled accuracy.

LLM Challenges and Limitations 

As with any other innovative technology, LLM use cases include success stories and challenges along the way. Take a look at the few most frequent ones, as well as at our pieces of advice on how to overcome them.

1. Ethical concerns and bias mitigation: Large language models (LLMs) may perpetuate or amplify biases in the data they were trained on, leading to unfair outcomes or discriminatory behavior in their applications. Biases can arise from the data used for training, including societal biases present in text corpora or data collection methods.

Mitigation tip: Businesses should invest in diverse and representative training data, implement bias detection and mitigation techniques during model development, and regularly audit their LLMs to ensure fairness and equity in their applications.

2. Computational resource requirements: Training and fine-tuning large language models require significant computational resources, including high-performance computing clusters and large-scale data storage facilities. LLMs have millions or even billions of parameters, necessitating large-scale distributed computing infrastructure for efficient training and fine-tuning.

Mitigation tip: Businesses can explore cloud-based solutions for scalable computing resources, optimize model architectures and training algorithms for efficiency, and prioritize computational resource allocation based on project requirements and budget constraints.

3. Fine-tuning for specific domains: While pre-trained LLMs offer broad language understanding, they may require fine-tuning for specific domains or industries to achieve optimal performance in specialized tasks.

Mitigation tip: Businesses should invest in domain-specific fine-tuning data and expertise, leverage transfer learning techniques to adapt pre-trained models to specific domains, and collaborate with domain experts to tailor LLMs to the unique requirements of their industry or use case.

1. Facebook-Cambridge Analytica scandal

In 2018, Cambridge Analytica, a political consulting firm, extracted personal data from millions of Facebook users without their permission. They utilized this information to craft tailored political ads for the 2016 US presidential election. The scandal raised concerns about privacy violations, data misuse, and the potential manipulation of public opinion through social media platforms.

2. Equifax data breach

In 2017, Equifax, a big American credit agency, had a huge data leak. It exposed personal info of around 147 million people, including names, Social Security numbers, birth dates, addresses, and sometimes even driver’s license numbers. The issue highlighted the vulnerability of centralized databases storing sensitive personal information and underscored the importance of robust cybersecurity measures to protect against data breaches and unauthorized access.

Lightpoint data security experts suggest adhering to these three recommendations to ensure data privacy and security when working with LLMs:

1. Implement robust data encryption: Encrypt sensitive data both at rest and in transit to prevent unauthorized access and data breaches. Employ strong encryption algorithms and encryption techniques such as Transport Layer Security (TLS) for securing data in transit, and Advanced Encryption Standard (AES) for encrypting data at rest.
2. Conduct regular security audits and assessments: Perform regular security audits and assessments to identify vulnerabilities and potential risks. Engage third-party cybersecurity experts to conduct comprehensive security assessments and penetration testing.
3. Implement privacy-preserving techniques: Employ differential privacy, federated learning, and homomorphic encryption to protect sensitive user data and maintain user privacy. Utilize data anonymization and pseudonymization to minimize the risk of data re-identification and unauthorized access.

By implementing these recommendations, businesses can mitigate the risks associated with data privacy and security when utilizing LLM software, safeguarding sensitive information, and maintaining user trust.

Future Trends and Developments

The introduction of large language models such as ChatGPT, which can answer questions and generate text, points to exciting possibilities in the future. Slowly but surely, LLMs are approaching human-like performance. Here are some thoughts on the advancements in LLM technology.

1. Increased abilities. Even though technology is amazing, it’s not flawless, and LLMs aren’t perfect either. However, newer versions will have improved accuracy and expanded features as developers learn to improve their performance while reducing bias and avoiding incorrect answers. For example, in healthcare, LLMs could assist in medical diagnosis by analyzing patient symptoms and medical histories, leading to more accurate diagnoses and treatment recommendations.
2. Audiovisual training. Many developers started training models with video and audio inputs. This form of training should lead to faster model development and open up new possibilities for using LLMs for autonomous vehicles.
3. Workplace transformation. LLMs are disruptors that will transform the workplace. LLMs are likely to reduce monotonous and repetitive tasks, just as robots reduce repetitive manufacturing tasks. Possibilities include repetitive office tasks, customer service chatbots, and simple automated copywriting.
4. Conversational AI. LLMs will surely make automated virtual assistants like Alexa, Google Assistant, and Siri work better. Businesses will get more precise understanding of what users want and handle complex commands more effectively, justifying the investment in technology.

Conclusion 

The global LLM market is expected to surge from $1,590 million in 2023 to around $259.8 million by 2030, with an impressive average growth rate of 79.80% during this period. This reflects a significant rise in the use and importance of LLMs across different sectors worldwide. Large language models will not only be trained to teach human language to AI applications but also be used for various other tasks, such as analyzing data structures, writing software code, and much more. 

Want to discover how your company can benefit from LLM’s problem-solving skills? We have developed a procedure to systematically analyze your challenges and guide you through a structured process:

• Define the tasks you want to achieve with the LLM
• Assess your computing capacity and infrastructure requirements
• Identify the data you want to integrate and use into the LLM

Contact the Lightpoint expert at any time to have a personal advice session.