ML Interview Questions

How do prompt injection attacks affect the safety and security of large language models (LLMs)? Discuss the potential risks these attacks pose to AI systems and user data. Explain various defense mechanisms that can be implemented to mitigate these risks, including examples of different types of prompt injection attacks and their potential impacts. Additionally, evaluate the effectiveness and limitations of these defense strategies, providing practical insights and considerations for their implementation.

Explain the concept of Support Vector Machines (SVM) in detail. Describe how SVMs perform classification, including the role of hyperplanes and support vectors. Discuss the importance of the kernel trick, and provide examples of different kernels that can be used. How do these kernels impact the decision boundaries?

Explain gradient boosting algorithms. How do they work, and what are the differences between XGBoost, LightGBM, and CatBoost?

Provide a comprehensive explanation of ensemble learning methods in machine learning. Compare and contrast bagging, boosting, stacking, and voting techniques. Explain the mathematical foundations, advantages, limitations, and real-world applications of each approach. When would you choose one ensemble method over another?

Describe and compare different techniques for anomaly detection in machine learning, focusing on statistical methods, distance-based methods, density-based methods, and isolation-based methods. What are the strengths and weaknesses of each method, and in what situations would each be most appropriate?

Describe the process and components of Reinforcement Learning from Human Feedback (RLHF) in the context of training large language models (LLMs). Discuss how RLHF incorporates key elements such as reward model training and proximal policy optimization (PPO). Furthermore, explore the challenges faced in aligning LLMs with human preferences using RLHF, and evaluate the limitations of this approach. What are some alternative methods being explored for improving alignment in LLMs?

Define and discuss the concept of model alignment in the context of large language models (LLMs). How do techniques such as Reinforcement Learning from Human Feedback (RLHF) contribute to achieving model alignment? Why is this important in the context of ethical AI development?

Explain the architecture and functioning of Generative Adversarial Networks (GANs). Discuss their key components, typical challenges encountered during training, and highlight some recent advancements in GAN technology.

Describe the Transformer architecture in detail, focusing on its key components such as the attention mechanism and positional encoding. Discuss how these components contribute to its success in natural language processing (NLP) tasks and compare it to traditional RNN-based models. How can Transformers be adapted for tasks beyond NLP, such as image processing or time series forecasting?

Explain attention mechanisms in deep learning. Compare different types of attention (additive, multiplicative, self-attention, multi-head attention). How do they work mathematically? What problems do they solve? How are they implemented in modern architectures like transformers?