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An Intro to LLMs and Generative AI

Originally posted on ODBMS

What are large language models? How do they relate to generative AI? Why do enterprises struggle to implement generative AI at scale?

As AI progresses and evolves in the enterprise, there are many new terms and technologies that may be unfamiliar. Here’s a quick rundown of the most common questions we receive.

What is a Large Language Model (LLM)?

A large language model (LLM) is a type of machine learning model that is trained on massive amounts of text data to generate natural language text. LLMs are neural network-based models that use deep learning techniques to analyze patterns in language data, and they can learn to generate text that is grammatically correct and semantically meaningful.

LLMs can be quite large, with billions of parameters, and they require significant computing power and data to train effectively. The most well-known LLMs include OpenAI’s GPT (Generative Pre-trained Transformer) models and Google’s BERT (Bidirectional Encoder Representations from Transformers) models. These models have achieved impressive results in various natural language processing tasks, including language translation, question-answering, and text generation.

How do you define in a nutshell Generative AI ?

Generative AI is the category of artificial intelligence that enables us to generate new content — this is an umbrella category that includes text generation from large language models, but also includes image and video generation or music composition. 

Generative AI models can also be used for more practical applications, such as creating realistic simulations or generating synthetic data for training other machine learning models. Overall, generative AI has the potential to revolutionize various industries, such as entertainment, marketing, and education, by enabling machines to create new and unique content that can be used for a wide range of purposes.

Did you use GPT-4? Are you happy about it?

I love GPT-4. Personally, it is most helpful for me when I need to summarize or make my text more concise. I also love using it to get a general overview of a topic, however I am really careful with the outputs it provides me. One of the challenges with LLMs is that they can sometimes generate “hallucinations,” which are responses that are factually incorrect or not related to the input prompt.

This phenomenon occurs because LLMs are trained on vast amounts of text data, which can sometimes include incorrect or misleading information. Additionally, LLMs are designed to generate responses based on statistical patterns in the data they are trained on, rather than a deep understanding of the meaning of the language.

As a result, LLMs may occasionally generate responses that are nonsensical, off-topic, or factually inaccurate. To mitigate the risk of hallucinations, researchers and developers are exploring various techniques, such as fine-tuning LLMs on specific domains or using human supervision to validate their output. Additionally, there are ongoing efforts to develop explainable AI methods that can help to understand how LLMs generate their output and identify potential model bias or errors.

What are the most common problems organizations face in deploying generative AI at scale ?

Deploying generative AI at scale can be a complex and multifaceted process that requires careful planning and execution. The MLOps lifecycle needs to be updated for generative AI. Large language model operations (LLMOps) is the updated framework to standardize LLMs. One of the biggest challenges that organizations face when deploying generative AI at scale is ensuring the quality of the data used to train the models. Generative AI models require large amounts of high-quality data to produce accurate and reliable results, and organizations must invest in data cleaning and preprocessing to ensure that the data is representative and unbiased.

Another challenge is the need for significant computational resources to train and run generative AI models at scale. Additionally, the lack of interpretability and explainability in generative AI models can pose challenges in applications where transparency and accountability are important. This can be problematic, especially in applications where accuracy is critical, such as in healthcare or legal contexts.

Ethical considerations are also critical when deploying generative AI at scale, as these models have the potential to generate biased or discriminatory content if not properly designed and trained. Organizations must be proactive in addressing these ethical considerations and take steps to mitigate potential risks.

Finally, integrating generative AI models with existing systems and workflows can be challenging and time-consuming. Organizations must be prepared to invest in the necessary resources and expertise to ensure that the models can be seamlessly integrated with existing systems and workflows. Overall, deploying generative AI at scale requires a comprehensive and strategic approach that addresses the technical, ethical, and organizational challenges involved.

What are the key risks of today’s generative AI stack?

Generative AI is a powerful technology with many applications, but it also poses several risks and challenges. These risks include bias and discrimination, privacy and security concerns, legal and ethical considerations, misinformation and disinformation, and the lack of interpretability and explainability. Generative AI models can generate biased or discriminatory content, leading to the reinforcement of existing biases or perpetuation of stereotypes. They can also be used to generate convincing fake content, such as images or videos, which can pose risks to individual privacy and security. Legal and ethical considerations arise with respect to intellectual property rights and liability for the content generated by the models. The technology can be used to generate false or misleading content, posing implications for society and democracy. Generative AI models are often difficult to interpret and explain, making it challenging to identify potential biases or errors in their output. It is crucial to approach the use of generative AI with care, planning, and oversight, to ensure ethical use of the technology as part of a responsible AI framework.

Self-supervised training on a large corpora of information leads to the model inadvertently learning unsafe content and then sharing it with users. What is your take on this?

The inadvertent learning of unsafe content by generative AI models trained on large corpora of information is a significant concern, and organizations must take steps to mitigate this risk. This risk arises because generative AI models learn from the data they are trained on, and if this data contains unsafe or harmful content, the models may inadvertently reproduce this content in their output.

One way to mitigate this risk is to carefully curate and preprocess the data used to train the models. This may involve removing or filtering out content that is known to be unsafe or harmful, or using human supervision to ensure that the data is representative and unbiased.

Another approach is to use techniques such as adversarial training or model debiasing to identify and remove unsafe content from the model’s output. This involves training the model to recognize and avoid unsafe content by providing it with examples of harmful content and encouraging it to generate safe and appropriate responses.

Ultimately, the risks associated with generative AI models learning and reproducing unsafe content underscore the need for careful planning and oversight in the deployment of these models. Organizations must take steps to ensure that the models are trained on high-quality data and that appropriate measures are in place to detect and mitigate potential risks.