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What does in-context learning in Large Language Models involve?

• A.Training the model using reinforcement learning
• B.Conditioning the model with task-specific instructions or demonstrations
• C.Pretraining the model on a specific domain
• D.Adding more layers to the model

What does the term "hallucination" refer to in the context of Large Language Models (LLMs)?

• A.The model's ability to generate imaginative and creative content
• B.The process by which the model visualizes and describes images in detail
• C.A technique used to enhance the model's performance on specific tasks
• D.The phenomenon where the model generates factually incorrect information or unrelated content as if it were true

An AI development company is working on an AI-assisted chatbot for a customer, which happens to be an online retail company. The goal is to create an assistant that can best answer queries regarding the company policies as well as retain the chat history throughout a session. Considering the capabilities, which type of model would be the best?

• A.A pre-trained LLM model from Cohere or OpenAI.
• B.A keyword search-based AI that responds based on specific keywords identified in customer queries.
• C.An LLM dedicated to generating text responses without external data integration.
• D.An LLM enhanced with Retrieval-Augmented Generation (RAG) for dynamic information retrieval and response generation.

What is prompt engineering in the context of Large Language Models (LLMs)?

• A.Training the model on a large dataset
• B.Adding more layers to the neural network
• C.Adjusting the hyperparameters of the model
• D.Iteratively refining the ask to elicit a desired response

Which is a characteristic of T-Few fine-tuning for Large Language Models (LLMs)?

• A.It does not update any weights but restructures the model architecture.
• B.It updates all the weights of the model uniformly.
• C.It increases the training time as compared to Vanilla fine-tuning.
• D.It selectively updates only a fraction of the model's weights.