July 3, 2024
Curious to know more about what an LLM is, and how Large Language Models are revolutionizing artificial intelligence and natural language processing? These state-of-the-art AI models use advanced deep learning architectures and an enormous number of parameters to perform particular tasks like text generation, translation, summarization, and question answering extremely well. From enhancing content creation to accelerating software development, LLMs are beneficial in a wide range of applications. In this post, we’ll explain what LLMs are, what they can do, what they can’t do, and what’s in store for the future of LLMs, so you can understand what’s on the horizon for AI.
What are Large Language Models (LLMs)?
Large language models (LLMs) are highly sophisticated AI systems that have been trained on an enormous amount of text data to understand and generate human-like language. They’re powered by deep learning, and specifically, a transformer architecture with self-attention. With billions of parameters, LLMs are capable of learning to understand and produce all sorts of complex language patterns and relationships, and they perform very well on a variety of natural language processing (NLP) tasks including text generation, translation, summarization, and question-answering.
Some popular LLMs include models from the GPT (Generative Pre-trained Transformer) series, BERT (Bidirectional Encoder Representations from Transformers), and T5 (Text-to-Text Transfer Transformer), which have significantly advanced AI and NLP, unlocking a whole new world of applications and use cases.
Large language models have been one of the influencers of generative AI. They are able to read and write human-like text and companies like IBM have been incorporating LLMs such as the Granite series of models on watsonx.ai into a wide range of products and services including watsonx Assistant and watsonx Orchestrate.
Those models can be used by businesses to solve many different kinds of problems. Text generation and content summarization, AI assistants, code generation, sentiment analysis, language translation, and more. LLMs leverage deep learning, transformer architecture, and scale to handle and generate text, so they understand context and can reply coherently as well as complete a range of natural language processing tasks.
As large language models (LLMs) become more prevalent, it’s critical that we put governance in place to use these powerful AI models responsibly and ethically. When we deploy LLMs we need to consider trust, transparency and security to take advantage of their capabilities while mitigating the risk.
IBM has things like the Granite models and watsonx.ai which offers access to pre-trained models, conversational AI capabilities, and tools and APIs for AI builders. And with these tools, LLMs are more readily available and more easily applied to all kinds of applications in all industries which continues the drive of the evolution of the digital landscape.
Large Language Models (LLMs) work by trying to predict the next word in a sequence given the context from the previous words, by deeply understanding language patterns and relationships, and being trained on a bunch of text. The model is fed a huge corpus of books, articles, websites, and other written materials during training— the idea is to give it every possible example of human language so it can absorb all the nuances of how we speak and write, and the complex ways words relate to one another. Having seen all this in training, the model can then predict the most probable next word for a given string of words, and therefore produce text that sounds natural and makes sense.
Training LLMs involves feeding the model large volumes of text data, which is then iteratively manipulated through a process called tokenization. Tokenization takes the raw text and divides it into smaller, meaningful parts called tokens that are easier for the model to interpret and act upon. This tokenization step typically uses complex algorithms such as byte pair encoding (BPE) to create an efficient vocabulary of tokens representing the building blocks of language. By uncovering the relationships between these tokens, the LLM is able to generate a nuanced understanding of language that extends well beyond single-word meanings.
Many LLMs use a Transformer under the hood, which leverages self-attention to recognize the relationships between words in a sequence all at once, rather than in the linear manner that traditional language models operate. Thanks to self-attention, the model can take in the meaning of a whole block of text all at once, rather than processing word by word. This self-attention is what enables the LLM to understand the complex relationships between words, and lets it generate more coherent text.
At generation time, when the LLM is asked to produce new text, it’s really just making a sequence of guesses about what the most likely next token is given the input context and the tokens it has generated so far. The key here is the word “guessing.” There are lots of ways to have a model make a guess—like beam search, which just tries a bunch of probable sequences of text to let the model spit out something coherent and meaningful. But ultimately, the LLM is guessing what the next token is, and it’s guessing for every token in the output. This sort of iterative token prediction and generation is also what makes LLMs so good at text generation—they just guess what the next token is, for every token, a whole bunch of times.
LLMs have a knack for generating impressive text. The results can be believable, readable, even useful. But they’re flawed. In its current form, GPT might not be the key to building real intelligence. Because it’s just predicting one token after another, not truly understanding language or the world. But with each new advance in LLMs, we could be one step closer to bigger, more capable models that break the boundaries of generative AI—and perhaps groundbreaking progress that could reshape how we experience and apply language technology.
Big language models (LLMs) are game-changers for content creation in any industry. With their massive knowledge of language structure, context and meaning, they can produce original written work quickly and at scale. Articles and essays, creative writing, marketing copy—whatever you need, LLMs can produce it to engage and delight your audience. And with content generation happening in the background, you get to spend your time reflecting, researching and planning—being a whole lot more efficient and effective.
Language customization for LLMs has changed the game when it comes to how we communicate and work together. With LLMs, you can put text into a system in one language and get highly accurate translations in lots of other languages out, and in a way that largely preserves the original meaning and nuance. This has enormous implications for businesses with multilingual customers or partners, for global businesses, and even for individuals who want to connect across language barriers. LLMs can be used for fast, easy translations of documents, web pages, and even spoken conversations on the fly to bridge linguistic barriers that have historically kept people from sharing knowledge and understanding each other, enabling deeper cross-cultural understanding.
LLMs have brought conversational AI to a new level, allowing chatbots and virtual assistants to have more natural, context-aware conversations—so you can ask a question, get a smart response, and even “shoot the breeze,” resulting in an experience that’s more personalized and intuitive. With LLMs, chatbots and assistants can help with everything from service inquiries, to setting appointments, to fetching information, resulting in happier users, more effective processes, and around-the-clock support options for companies.
The code-generating and autocompleting abilities of LLMs have been a game changer for coding. These AIs possess a knowledge of programming languages and can complete code snippets, suggest changes to code, or even write entire programs based on a natural language prompt. This allows software engineers to automate repetitive coding tasks, iterate and prototype more quickly, and focus on high leverage problem-solving and creativity. They can also explain and teach complex programming concepts, which makes it easier to understand and learn for newcomers as well as experts.
In today’s day and age, LLMs are a very powerful tool because they can condense really long documents into something that’s short, but still very informative. By understanding the main themes, main points, and structure of the text, LLMs can figure out what the most important parts of the document are and present it in a very logical, organized manner. This is very useful for people in all professions, including finance, law, academia, or any job where it’s critical to understand very long reports, research papers, legal documents, etc. Summarization using LLMs is a great way to become more productive, save time, and ensure you don’t miss any important insights.
LLMs are useful in a variety of industries for determining tone and sentiment of a given text. Whether you need to understand customer feedback, track social media posts, or gauge employee satisfaction, LLMs can guess the emotions and moods being expressed. This is valuable for a lot of things, because it helps organizations to operate better, deliver better service, keep people happy, and keep watch on trends/issues. When businesses can measure sentiment using LLMs, they can really hear the voices of their stakeholders and respond appropriately.
LLMs have really transformed the way we can access information and knowledge. These advanced AI systems can answer a vast array of questions with rich, contextual responses accurately and fast, thanks to their enormous training data and natural language processing. Whether in education, in customer support, in research – wherever, LLM-powered QA systems can access and answer questions quickly, helping users find the information they need faster – so they can make better decisions, learn more, delight customers, etc.
The era of large language models (LLMs) has revolutionized AI for good—and for good reason. These powerful models—like GPT-4—are already great at handling natural language processing and generation. Now, with additional capabilities, they’re capable of providing the kinds of exceptional functionality that could have a big impact on your software purchases and development.
LLMs can understand context, nuances and complex language structures better than traditional NLP models. With that improved understanding, they're able to understand what the user wants better, understand technical documentation better, and facilitate better communications between software teams and other stakeholders.
A single LLM can be used for multiple things which means you don’t need one-off models. This flexibility allows procurement professionals to use the same model for e.g. requirement gathering, or proposal evaluation, or both –saving time and effort in the long run.
LLMs can generalize to new tasks with very little or no task-specific training data. This means software acquisition teams can adapt to new needs and deploy models to new problems rapidly, making them more agile and faster.
LLMs can automate a lot of language work, which means less time and resources spent on content creation, customer support, and data analysis. Efficiency can mean the difference between a software company just barely getting by, and a software company that’s growing like crazy.
Many LLMs are trained on multilingual data sets, so they understand more than just English. This is useful if you are buying software and have international team members, suppliers or customers, because you can all speak and work together without any problems.
LLMs are trained on new data and specialized on particular domains, which means they can continue improving. As the software acquisition community applies the technology, they can reap the benefits of those improvements without doing anything.
LLMs enable software acquisition professionals to simplify their lives, be more productive, and foster innovation in their organizations. Just make sure you also know the challenges of LLMs so that you can minimize privacy, trust, and other risks, and ensure that you meet any requirements specific to your industry or domain.
Hallucinations: LLMs can’t really produce reliable, truthful output because they don’t understand anything. These models are just memorizing the statistical patterns in their training data to produce semi-believable output, but they can’t distinguish fact from fiction. Since LLMs lack any real knowledge, they can unintentionally hallucinate output that may look believable but is actually 100% fabricated. This hallucination behavior is extremely problematic, because it means LLMs can just make stuff up. Researchers are working hard to develop better detectors and mitigators for hallucination, but it remains a sticking point when it comes to developing robust, trustworthy LLMs.
Bias: The training data used to train LLMs tends to be biased due to societal biases, which LLMs can pick up and regurgitate. This can manifest in many ways, from propagating stereotypes, to being unfair towards certain groups, to preferring certain viewpoints/narratives. Addressing and neutralizing bias in LLMs is a difficult, multi-faceted issue that needs to be attacked from all angles. Developers need to be diligent and careful with their data, debias their models, and monitor and audit their models on an ongoing basis to catch and mitigate bias. Ensuring LLM-powered applications are fair and inclusive is critical, since they are being implemented in more and more high-stakes scenarios.
No Understanding: LLMs can spit out fluent, coherent text all day long, but at the end of the day, they aren’t having the lightbulb moments that humans have. These models are only picking up on statistical patterns and repeating them as believable-sounding output, but they are not actually understanding the content they are producing. This comes with a long list of drawbacks, like being unable to reason, being unable to draw meaningful conclusions, being unable to handle new scenarios, and so on and so forth. You might find that LLMs are producing nonsensical output, or you might find that they are producing output that looks good on the surface but is actually missing crucial information. Overcoming this and developing LLMs that are truly, contextually competent is a difficult problem in NLP.
High compute costs: Building and deploying massive, state-of-the-art LLMs requires a huge amount of compute—lots of training data, specialized hardware and energy-intensive compute infrastructure. As these costs continue to increase, the financial and environmental cost of training and running LLMs becomes more and more prohibitive, preventing smaller or less-resource-rich organizations from being able to utilize these powerful AI systems. LLMs might grow bigger and more powerful, but as long as compute costs create a bottleneck, the world will be divided into “LLM-haves” and “LLM-have-nots”.
Data privacy: LLMs are typically trained with a process like “scrape all the data from the internet”, which brings with it a slew of considerations around people’s privacy and rights. Collecting, aggregating, and using this data—some of which may be personally identifiable, or touch on sensitive topics, or reflect users’ browsing history—carries ethical and legal hazard. Handling data responsibly and accountably is a key component of trust in applications built on LLMs. Researchers and developers should be laser-focused on good data practices, including minimal use of data and use of privacy-preserving techniques, to make sure it can’t be used inappropriately.
Ethical considerations: LLMs are extremely powerful and can be used for abuse, which raises lots of ethical considerations. They can generate realistic looking but fake content such as misinformation, hate speech, or deepfakes which can cause a lot of harm and damage. And because they can generate communications that are indistinguishable from human, there’s a lot of potential for deception and manipulation, which in turn can lead to eroding trust in digital communications and perpetuating harmful narratives. Mitigating these risks is going to require a lot of governance, airtight content moderation, and clear guidelines for responsible usage of LLMs. Researchers, policymakers, and industry need to work together to ensure the powerful tech is used in a way that’s beneficial for society and maximizes the societal good.
Explainability: LLMs are super complex and difficult to understand, let alone interpret why they made the decisions they made. They’re a bit of a black box, so it’s difficult to do a post-mortem on why they outputted what they did what influenced their decision-making process. This lack of transparency and explainability has a lot of people concerned about how to even begin to hold these applications accountable, and whether or not they are even fair. Whether or not we can even trust these applications. We’re going to need to develop techniques to make these LLMs interpretable and explainable so that we can trust them, so that we can hold them accountable, so that we can ensure that they are being used responsibly and ethically. Recent advances in explainable AI, and integrating transparency-enhancing mechanisms into LLM architectures are a step in the right direction.
Big language models are behind some of the most exciting AI advancements. Here’s what’s next for the technology, and how we can develop and harness the power of LLMs effectively.
We’re also working on research to reduce hallucinations and increase the realness of what LLMs output. By making the models have a better understanding of the world, and also better at knowing what’s true and what’s not, LLMs will be a reliable source of information and analysis. This’ll be super important for applications where you really need it to be reliable, such as in healthcare, finance, and making policy decisions!
Future LLMs might incorporate text, image, audio, and video processing for a more comprehensive understanding and generation. This multimodal approach will allow LLMs to handle complex, real-world scenarios much more effectively, bridging the gap between linguistic and non-linguistic information. These advancements will pave the way for more intuitive and immersive human-AI interactions.
Model compression, quantization, and hardware improvements will democratize and economize LLMs. By reducing the compute and energy costs of training and inferencing with these models, more organizations and individuals will be able to leverage LLMs for discovery and enrichment.
Specialized models for specific industries or applications will emerge, and they’ll be better in that specific niche. These industry-specific LLMs will be fine-tuned on relevant data and will come prepackaged with industry knowledge that only that niche would know about, so they can give you an even more accurate and relevant output for your use case.
We’re focused on establishing strong standards and tools for responsible LLM deployment. As these models become more powerful, we need to be thinking about ethical considerations (bias mitigation, privacy, transparency in decision-making), etc. Ethical AI frameworks and standards will ensure equitable distribution of LLMs’ benefits, and use that aligns with our societal values.
Transfer learning is a method that allows LLMs to learn new tricks, with very little extra training. It will allow LLMs to learn from one situation and apply it to a wide variety of problems all in good time, reducing the need for data collection and model retraining.
LLMs will become part of a suite of AI technologies that also include reinforcement learning, and knowledge graphs, to unlock even more powerful and robust AI performance. When combined, these AI system of systems will be capable of tackling complex problems that require language understanding, reasoning, and decision-making.
The future of LLMs is bright, and these capabilities will facilitate increasingly powerful, personalized, and reliable AI applications. As the technology develops, cooperation between developers, researchers, and potentially even policy makers will be key to ensuring LLMs are developed and applied responsibly and ethically.
| Accuracy | Makes stuff up | Improved fact-checking, fewer mistakes |
|---|---|---|
| Capabilities | Text only | Multimodal (text, image, audio, video) |
| Efficiency | Compute-heavy | Model compression, hardware efficiency |
| Specialization | General LLMs | Custom LLMs for very specific tasks |
| Ethics | Bias, privacy | Strong ethical AI frameworks and guidelines |
| Adaptability | Limited few-shot learning | Better transfer learning |
| Integration | Standalone | In concert with other AI technologies (RL, knowledge graphs) |
What are large language models?
Large language models (LLMs) are AI systems trained on lots of text data using a transformer and self-attention to perform really well on a wide variety of natural language tasks. With models like GPT, BERT, T5, and others, LLMs are transforming AI and NLP. They should be used responsibly, and considerations of trust, transparency, and governance are important when they are used.
How do large language models work?
LLMs predict the next word, use transformer, and use self-attention to generate coherent text. This makes them useful for content creation, translation, code generation, chatbots, summarization, sentiment analysis, question-answering, and more.
What are the benefits and drawbacks of large language models?
There are many advantages to using LLMs, such as improved understanding, flexibility, efficiency, multilingualism, and continual learning. But there are also lots of disadvantages, like hallucination, bias, lack of true understanding, high compute requirements, data privacy concerns, ethical issues, and lack of explainability.
What is the future of large language models?
In the future, LLMs will be more accurate, multimodal, trained more efficiently, have domain-specific models, have ethical AI frameworks, learn better, and work well with other AI technologies.
Large Language Models (LLMs) are AI systems trained on a lot of text data that learn to understand and generate human-like language by predicting the next word in a sequence given all the previous words. Using transformer architecture with self-attention, LLMs are able to understand complex language relationships and perform very well on a variety of NLP tasks.
Popular large language models like GPT, BERT, and T5 have transformed artificial intelligence and natural language processing, unlocking new possibilities for text generation, translation, summarization, question-answering and more that are applicable across many different industries.
LLMs have been used for content generation, language translation, chatbots, code generation, text summarization, sentiment analysis, and question answering and have been used to revolutionize everything from automating content creation to improving user experiences with virtual assistants, code generation, and advanced language analysis.
You can use LLMs to enhance natural language understanding, do a lot, facilitate few-shot and zero-shot learning, help you automate language-oriented tasks, work in multiple languages and be updated and fine-tuned with updates and domain-specific models.
Challenges and limitations of LLMs include hallucinations, bias amplification, lack of true understanding, high compute requirements, data privacy concerns, ethical considerations, and interpretability issues. All of these require careful strategies to mitigate to ensure responsible and ethical deployment of LLM-powered applications.
Future LLM developments will trend toward increasing accuracy and reliability, multi-modality, more efficient training and inference, domain-specific LLMs, ethical AI guidelines, better few-shot / zero-shot learning, and synergy with other AI technologies will mean even more intelligent, more personalized, more reliable AI applications.
