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What is the primary purpose oi inferencing in the lifecycle of a Large Language Model (LLM)?
Inferencing in the lifecycle of a Large Language Model (LLM) refers to using the model in practical applications. Here's an in-depth explanation:
Inferencing: This is the phase where the trained model is deployed to make predictions or generate outputs based on new input data. It is essentially the model's application stage.
Production Use: In production, inferencing involves using the model in live applications, such as chatbots or recommendation systems, where it interacts with real users.
Research and Testing: During research and testing, inferencing is used to evaluate the model's performance, validate its accuracy, and identify areas for improvement.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep Learning. Nature, 521(7553), 436-444.
Chollet, F. (2017). Deep Learning with Python. Manning Publications.
What is the primary purpose of fine-tuning in the lifecycle of a Large Language Model (LLM)?
Definition of Fine-Tuning: Fine-tuning is a process in which a pretrained model is further trained on a smaller, task-specific dataset. This helps the model adapt to particular tasks or domains, improving its performance in those areas.
Purpose: The primary purpose is to refine the model's parameters so that it performs optimally on the specific content it will encounter in real-world applications. This makes the model more accurate and efficient for the given task.
Example: For instance, a general language model can be fine-tuned on legal documents to create a specialized model for legal text analysis, improving its ability to understand and generate text in that specific context.
A company is implementing governance in its Generative Al.
What is a key aspect of this governance?
Governance in Generative AI involves several key aspects, among which transparency is crucial. Transparency in AI governance refers to the clarity and openness regarding how AI systems operate, the data they use, the decision-making processes they employ, and the way they are developed and deployed. It ensures that stakeholders understand AI processes and can trust the outcomes produced by AI systems.
User interface design (Option OB), speed of deployment (Option OC), and cost efficiency (Option OD) are important factors in the development and implementation of AI systems but are not specifically governance aspects. Governance focuses on the overarching principles and practices that guide the ethical and responsible use of AI, making transparency the key aspect in this context.
Why should artificial intelligence developers always take inputs from diverse sources?
Diverse Data Sources: Utilizing inputs from diverse sources ensures the AI model is exposed to a wide range of scenarios, dialects, and contexts. This diversity helps the model generalize better and avoid biases that could occur if the data were too homogeneous.
Comprehensive Coverage: By incorporating diverse inputs, developers ensure the model can handle various edge cases and unexpected inputs, making it robust and reliable in real-world applications.
Avoiding Bias: Diverse inputs reduce the risk of bias in AI systems by representing a broad spectrum of user experiences and perspectives, leading to fairer and more accurate predictions.
What is feature-based transfer learning?
Feature-based transfer learning involves leveraging certain features learned by a pre-trained model and adapting them to a new task. Here's a detailed explanation:
Feature Selection: This process involves identifying and selecting specific features or layers from a pre-trained model that are relevant to the new task while discarding others that are not.
Adaptation: The selected features are then fine-tuned or re-trained on the new dataset, allowing the model to adapt to the new task with improved performance.
Efficiency: This approach is computationally efficient because it reuses existing features, reducing the amount of data and time needed for training compared to starting from scratch.
Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345-1359.
Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How Transferable Are Features in Deep Neural Networks? In Advances in Neural Information Processing Systems.
