What is a Large Language Model?

A large language model is a type of artificial intelligence that employs deep learning to comprehend and produce human language. These models are called “large” because of the vast amount of information they must be trained on to learn how to perform a wide range of language-related tasks, such as responding to queries, translating languages and creating other forms of content. LLMs are sometimes referred to as neural networks (NN) — computer systems that emulate the human brain — because they rely on a network of neuron-like nodes. They are constantly being trained and upgraded to improve their problem-solving abilities and make them as fluid and effective as possible.  

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How do LLMs work?

LLMs learn the structure of language (including grammar, vocabulary and context) by processing massive volumes (in the billions of pages) of text. The more data an LLM is exposed to, the better it becomes at understanding, interpreting and using language — not unlike the human brain after reading hundreds of novels. One underlying technology that helps facilitate the process is known as transformer architecture. Transformers help an LLM understand the context of a word by analyzing the words that come before and after it. The more examples of text it analyzes, the better a model becomes at predicting the next word in a sentence. 

Once a model has been trained using a sufficient number of parameters, it is able to generate contextually relevant, coherent text. When you input a prompt, it is able to produce a response based on the information used to train it. LLMs are constantly being fed more and more content, improving their ability to understand more complex language and generate accurate responses more quickly.

 Why you should care

 LLMs are growing in popularity and use

Their ability to understand text and voice prompts make LLMs excellent chatbots and virtual assistants. When a user enters a query, an LLM processes the input and uses the patterns it has learned over time to produce meaningful responses. Chatbots are so good at interacting with humans that it has become difficult to tell if there is a human or machine on the other end of a conversation.

How else are LLMs being used?

LLMs have proven vastly useful to a number of different industries. In health care, for example, LLMs are used to identify patterns that may point to the presence of disease, including cancer and diabetes. They are also used to recommend appropriate treatment and help make it less likely that a patient will be misdiagnosed.

In the world of finance, they are exceptionally good at analyzing market trends and providing customers with personalized advice. They are also able to detect fraudulent activities by identifying unusual transaction patterns.

In education, LLMs are used to support personalized learning by explaining complex subjects, creating tailored study plans and providing real-time feedback on how students are performing.

How you can use LLMs today 

Interact with a chatbot

Using Chat GPT, or another chatbot, is a great way to interact with an LLM. You can start by asking a few straightforward requests to get a better sense of how LLMs work and what they can do. Ask it to perform a task — such as writing an email or text message according to certain criteria — and see what it creates for you. You can get it to change or improve the copy by adding more detailed information. The more experience you have interacting with an LLM, the easier and more effective the process will become. 

Common Concerns with LLMs

Biases and inaccuracies

LLMs are a product of the information used to train them, meaning bias and inaccurate information may be inadvertently generated. Depending on the data used, this can also mean certain viewpoints will be under- or over-represented, leading to misleading or skewed responses. 

Improving the quality of the data LLMs are trained on reduces the amount of bias or misinformation and allows for more diverse representation. Bias detection and mitigation algorithms are being used to detect and eliminate the presence of inaccuracies and misinformation. Human oversight allows for detection and removal of faulty information and adjustments to make responses more accurate.

Using LLMs responsibly

As with any information you access online, it’s important to verify the information you receive from an LLM. Simply being aware that these models may reflect the biases of society will help you apply critical thinking to the responses you receive. Avoid inputting too much of your own personal information as that data may be used in ways you can’t control. Keep in mind that LLMs are good at providing ideas, summaries and context, but not necessarily deeper levels of expertise.

The Risks of LLMs

National defence challenges

The growing sophistication of LLMs present a number of potential security risks related to military operations and strategic stability. 

Misinformation and Disinformation: There is a risk that LLMs could be used to undermine public trust, influence elections or generate social unrest through the spread of propaganda or false information. 

Cybersecurity Threats: LLMs could be used to execute increasingly sophisticated cyberattacks, including phishing campaigns and social engineering. They can make it easier for adversaries to trick individuals or systems into compromising security protocols.

Autonomous Weapons: Incorporating LLMs into autonomous systems, such as drones or robots, raises concerns about the reliability and safety of decision-making in critical situations. Ensuring these systems act within ethical and legal boundaries is crucial.

Intelligence Gathering and Analysis: LLMs ability to analyze large volumes of data carries advantages and risks. Adversaries might be able to use these models to analyze open-source intelligence, enhance their own capabilities or identify vulnerabilities in national security systems.

Operational Security: The use of LLMs for military communication or decision-making could lead to inadvertent leaks or exposure of sensitive information. Ensuring these systems are secure is vital.

Ethical and Legal Implications: The deployment of LLMs in national defense raises ethical and legal questions around accountability, transparency, and compliance with international laws and norms. It is important to ensure that LLMs adhere to legal standards and ethical considerations.

Capability Parity: The rapid advancement of LLM technology could lead to imbalances in the capabilities between different nations or organizations, potentially creating new power imbalances or exacerbating old ones.

Free speech issues

Censorship and moderation: The ability of LLMs to filter out content raises concerns about how to balance free speech with harm prevention. The subjective nature of what is inappropriate — or harmful — opens up LLMs to concerns about unfair censorship.

Bias and Discrimination: If the information an LLM is trained on contains biased data, it might spread these prejudices, limit perspectives and produce a skewed version of free speech.

Transparency and Accountability: There will undoubtedly be accountability concerns — including those concerning free speech — when it comes to decisions made by algorithms.

Autonomous Content Generation: LLMs ability to produce vast amounts of content raises questions about who is responsible for what is produced and how it aligns with free speech. This touches on issues related to content ownership and the potential for the misuse of intellectual property.

Dangerous use 

The way in which LLMs work could be exploited in a number of harmful ways, such as revealing how to create explosive devices or facilitating other illegal activities. It is vital that LLMs are carefully curated and use filters and other security measures to prevent the widespread dissemination of dangerous information. LLMs can push back against such use cases by being as transparent as possible about how they are used and by working with law enforcement to develop policies that guard against misuse.

Hacking Concerns

There are a number of security measures and best practices LLM creators can use to decrease the likelihood of their models being hacked or otherwise used maliciously. These include scrutinizing user provided content to filter out anything that may be harmful and implementing filters to block known malicious patterns. They can also add a layer of human oversight to review questionable inputs before they become part of a model. Any company working on an LLM should also consider using output filtering to make sure harmful information is removed before it can be shared. Releasing regular updates of an LLM is a good way to close known vulnerabilities and keep LLMs as safe as possible.  

Companies creating LLMs should ensure only authorized users are granted direct access to any network hosting an LLM and use activity logs and anomaly detection to ensure only vetted people can access its source code. 

 The future of LLMs

As LLMs get smarter and more sophisticated, their popularity will continue to grow. In addition to gaining the ability to streamline more tasks for individuals and businesses, these models will become more personalized and integrated into other apps. There will likely be a push to make LLMs safer and less capable of producing harmful outputs, including the creation of rules and guidelines that govern how they should be used.