Challenges with large language models

- [Instructor] The arrival of ChatGPT in November 2022 marked a new era in conversational AI. ChatGPT was trained using a variant of the Transformer model on a diverse range of texts from the internet. However, unlike the models that came before it, ChatGPT was also fine-tuned using human feedback. This allowed it to do a better job of learning the nuances of human conversation. The simplicity with which users could interact with the model meant that anyone, regardless of technical ability, could now engage effectively with a powerful large language model. This led to a blend of excitement and apprehension due to the potential implications of such a powerful AI tool. As large language models grow in size, capability, and popularity, we have to be clear-eyed about their ethical implications and potential for misuse. One area of concern is regard to bias and prejudice. AI models are only as good as the data they are trained on. Large language models can reflect, and more importantly, amplify the biases present in their training data. This includes cultural, racial, gender and religious biases, biases that have a tangible impact on hiring decisions, medical care, or financial outcomes. Addressing bias in LLMs is an ongoing process that requires vigilance, transparency, collaboration, and a commitment to promoting fairness and equity in AI systems. While doing so, it's important to come to a consensus on a set of ethical guidelines on how AI tools like large language models are trained and used, decide what data they should or should not be exposed to, identify and document what criteria are used to determine what is appropriate or inappropriate training data, and decide whether LLMs should reflect our society as-is, or whether they should help us move to a better place by reflecting the society that we aspire to be. As large language models become better at understanding and mimicking human dialogue, their potential for generating and spreading misinformation and disinformation at an unprecedented scale also increases. Several bloggers and researchers have shown how easy it is to use an LLM to generate fake news articles. In one paper authored by OpenAI, impartial human judges were only able to correctly identify 52% of the articles written by an LLM as AI-created content. That's just slightly above mere chance. This raises significant ethical and safety concerns, which have serious consequences for individuals and society as a whole. Addressing misinformation and disinformation generated by LLMs is a complex issue that requires collaboration between technology developers, content platforms, fact-checkers, and regulators. It also requires a commitment to ethical AI practices and responsible information dissemination. To mitigate the risk of using LLMs for misinformation, it is essential that we implement mechanisms to verify and fact-check the information generated by these models, educate users about the limitations of large language models, label or mark content generated by LLMs to indicate that it's AI or machine generated, which fosters transparency. Promoting media literacy and critical thinking skills among users is also important to combat misinformation effectively. Another concern with large language models is interpretability and transparency. As large language models become larger and more complex, it becomes more and more difficult to understand how they make their decisions. Enhancing the transparency and explainability of these models is essential to build trust and accountability. To help with this, developers can provide comprehensive documentation detailing the architecture, training data, and limitations of their models. Incorporate explainable AI techniques, such as attention maps, feature attribution, and saliency maps to visualize model decisions. Promote collaboration between AI and human experts to validate and interpret complex model-generated content. Large language models require a significant amount of computational resources to train. Significant computational resources mean significant energy consumption, and for the most part, significant energy consumption means significant carbon emissions. In fact, researchers from the University of Copenhagen estimated that the carbon footprint generated in training OpenAI's GPT-3 was roughly the same as that of driving a car to the moon and back. To mitigate the environmental impact of training large language models, we can encourage or incentivize the use of energy-efficient hardware, choose data centers powered by renewable energy sources, optimize model architectures, data efficiency, parallelism, and early stopping, employ transfer learning by reusing pre-trained models, advocate for and support policies and regulations that encourage sustainable AI development practice, raise awareness within the AI research and development community about the environmental impact of large language model training, and promote best practices for sustainability. Large language models require tremendous amounts of data to train. At scale, understanding provenance, authorship, and copyright status is a gargantuan if not impossible task. This raises important privacy and copyright violation concerns, and unvetted training sets can result in a model that is leaking private data, misattributing citations, or plagiarizing copyrighted content. As we write prompts, it's important to note that we become increasingly susceptible to data leakage. By asking the chatbot to find the bug in our code or to write a sensitive document, we're sending that data to a third party who may end up using it for model training, advertising, or competitive advantage. Mitigating privacy and copyright violations in large language models involves a combination of technical, legal, and ethical approaches, some of which include carefully curating and preprocessing training data to remove sensitive or copyrighted content, regularly auditing and evaluating model outputs for privacy and copyright issues, collaborating with content creators and rights holders to secure licenses for copyrighted material, educating users about responsible and ethical model usage. While most LLMs perform well on tasks that they were trained on, they can struggle with tasks or inputs that are slightly different from what they were exposed to during training. In fact, transformers, which most modern large language models are built on, are prone to what are known as hallucinations. Hallucinations are words or phrases that are generated by the model that are often nonsensical or grammatically incorrect. It can occur as a result of a model not being trained on enough data, a model being trained on noisy or bad data, or a model not being given enough constraints. Hallucinations can be benign when they simply lead to output that is difficult to understand. However, hallucinations can also be harmful if they lead to a model generating incorrect or misleading information that has real-life consequences. A few of the things we can do to enhance the robustness and the ability of large language models to generalize include incorporating a wide variety of data sources and domains during training to expose the model to diverse contexts and information, fine-tuning LLMs on domain-specific or task-specific data to adapt them to specific applications and improve robustness, augmenting training data with variations, perturbations, or synthetic examples to improve the model's ability to generalize, incorporating human feedback to correct model errors and biases, and enhance generalization.