It’s probably fair to say that when non-data scientists talk about generative AI, they are likely thinking of some flavor of large language models (LLMs). Yet, a bit of nuance gets lost here: all LLMs are generative AI, but not all generative AI uses LLMs. This slight misconception is understandable, as the general public’s understanding of LLMs is still in its early stages. But as we’ve advocated before, the more you know, the more you can leverage generative AI successfully.
It’s like how football, soccer, basketball, golf, etc., are all sports, but not all sports use a ball (e.g., boxing, track, swimming, gymnastics, etc). Sure, it’s a basic concept, but in all the excitement about what generative AI represents for the future of government, industry, business, medicine, and more as the next productivity frontier, we should make sure we get the fundamentals straight.
Continuing on this theme, the following excerpt from chapter five, “Diving Deeper into Generative AI,” in SparkCognition’s founder and CEO Amir Husain’s new book, Generative AI for Leaders, provides a concise yet thorough treatment of LLMs—a specific type of model designed for natural language generation. This chapter is a prime example of how business leaders can use Amir’s book as a reference to better understand the world of generative AI so they can navigate it successfully on their organization’s behalf.
And remember, like the ballgames vs sports analogy above, Amir’s highly reviewed new book is called Generative AI for Leaders and not “LLMs for Leaders” for a reason.
Generative AI for Leaders can be found on Amazon in hardback, paperback, and Kindle—and is now available on Audible!
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Understanding LLMs
At their core, LLMs are neural network-based models designed to understand, generate, and manipulate human language. They are “large” due to the vast number of parameters they contain, often reaching into the billions. These parameters enable LLMs to learn from and generate complex, nuanced textual data, making them capable of remarkable tasks such as language translation, text summarization, and even creative writing.
Training LLMs
LLMs are trained on vast amounts of text data. They learn to predict the next word in a sentence given the preceding words, a process known as autoregressive language modeling. This mechanism allows LLMs to understand grammar, context, and even cultural references, metaphors, and idioms. The models are unsupervised, which means they learn directly from raw text data without needing explicit labels.
Capabilities of LLMs
The capabilities of LLMs are diverse and continually expanding. They can generate human-like text, answer complex questions, write code, and even create poetry or prose. They can understand sentiment, extract information, and summarize long documents. These capabilities have made LLMs invaluable across many industries, from customer service and content creation to healthcare and law.
Fine-Tuning LLMs
As we mentioned, LLMs have proven their value as versatile tools in a variety of applications. From generating human-like text to answering complex queries, these models, when properly trained, can perform impressive feats of AI prowess. However, their real power becomes evident when they are fine-tuned for specific tasks, industries, or companies. Fine-tuning an LLM involves taking the foundational model—which has been pre-trained on a broad corpus of data—and further training it on a specialized dataset relevant to the task at hand. This might be data from a particular field, like medical literature for healthcare applications or corporate documents for business applications. Fine-tuning effectively extends the model’s knowledge, making it more accurate and useful in the specific context in which it will be used.
The Art of Prompt Engineering
Despite their power, LLMs are not yet mind-readers. Extracting useful information from them requires skill and experience, a practice known as prompt engineering. This involves providing the LLM with context or cues—the prompts—that help it generate the desired output. The art of prompt engineering lies in understanding how the model “thinks” and crafting prompts that guide it towards the desired outcome without leading it too strongly or leaving it too vague. This might involve experimentation, iterating, and refining prompts based on the model’s responses. It is a form of dialogue between human and machine, requiring a deep understanding of both the task at hand and the workings of the model.
Data Leakage in Fine-Tuning and Prompt Engineering
While fine-tuning and prompt engineering can enhance the performance of LLMs, they also raise the risk of data leakage. This is when private or sensitive information, included in the training data or prompts, inadvertently makes its way into the model’s outputs, potentially making it accessible to others who use the model. For instance, if an LLM is fine-tuned on company documents that include proprietary information, the model might generate text that references or hints at this information. Similarly, prompts that contain sensitive details can lead the model to generate responses that include these details. Both cases represent data leakage, which can pose privacy and security risks.
Shared vs. Private LLMs
Large tech companies and cloud providers are increasingly offering “LLM as a service,” where users can access pre-trained models via the cloud. These shared models offer advantages such as convenience and minimal IT setup overhead but raise major issues around data privacy and control. An alternative is for a company to develop its own LLM. However, training an LLM from scratch is a daunting task requiring substantial resources. A more feasible option for many is to start with an open-source foundational model and fine-tune it on their own data. This offers a balance between control and feasibility, allowing companies to leverage the power of LLMs while maintaining control over their data and model behavior.
Hallucinations in LLM Outputs
While LLMs can generate remarkably human-like text, they’re not perfect. One notable issue is the tendency to “hallucinate,” or generate information that wasn’t in their training data and isn’t true. This is because LLMs are optimized for creativity and coherence, not factual accuracy. In applications where accuracy is critical, this can be a problem. One strategy is to use LLMs in contexts where brain-storming is useful, or where outputs can be verified. This harnesses the creative power of the models while mitigating the risks of their hallucinations.
Transparency and Explainability
LLMs, like many neural networks, are often described as “black boxes.” Their decision-making processes are hard to interpret, and their outputs can sometimes be surprising or difficult to explain. This lack of transparency and explainability is a fundamental limitation of the technology, and it raises challenges in terms of accountability, trustworthiness, and regulatory compliance.
Despite these challenges, progress is being made. Researchers are developing methods for better understanding the internal workings of these models, as well as techniques to make them more interpretable and controllable. However, these are active areas of research, and full transparency and explainability are still some way off.
It is worth noting that, while some LLM offerings may claim to offer full transparency and explainability, these claims should be viewed with caution. Given the current state of the art, such claims are often overblown and can be misleading. A thorough evaluation and understanding of the technology is necessary before any deployment. Ultimately, transparency and explainability in AI should be about building systems that operate in a way that is understandable and trustworthy to humans.
The Power of Transfer Learning
One of the key strengths of LLMs is their ability to leverage transfer learning. After being trained on a large corpus of text, an LLM can be fine-tuned on a specific task with a much smaller dataset. This process enables the model to adapt its vast language knowledge to specific applications, making it an efficient solution for numerous NLP tasks.
Understanding Context with Transformers
A major breakthrough in LLMs was the introduction of the Transformer architecture, a model structure that uses self-attention mechanisms to understand the context of each word in relation to all other words in a text. This capacity to capture both local and global context has significantly improved the performance of LLMs, enabling them to generate more coherent and contextually accurate text.
Handling Ambiguity
Language is often ambiguous and context-dependent, which can make it challenging for machines to understand. LLMs, however, can handle such ambiguity effectively. By considering the broader context, LLMs can disambiguate words and understand nuanced meanings, enabling them to produce more accurate and contextually appropriate outputs.
Challenges and Limitations of LLMs
Despite their impressive capabilities, LLMs also have their challenges. They require vast amounts of data and computational resources, which can make them inaccessible for some organizations. They can also inadvertently generate biased or harmful content if such content is present in their training data. Additionally, while LLMs can generate coherent text, they do not truly understand the text in the way humans do. They do not have beliefs, desires, or intentions, and their outputs are purely a reflection of the patterns they have learned from their training data.
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Generative AI for Leaders can be found on Amazon in hardback, paperback, and Kindle—and is now available on Audible!
