What does a cosine distance of 0 indicate about the relationship between two embeddings?
Which statement is true about Fine-tuning and Parameter-Efficient Fine-Tuning (PEFT)?
Which LangChain component is responsible for generating the linguistic output in a chatbot system?
In the simplified workflow for managing and querying vector data, what is the role of indexing?
What issue might arise from using small datasets with the Vanilla fine-tuning method in the OCI Generative AI service?
What is the main advantage of using few-shot model prompting to customize a Large Language Model (LLM)?
What does accuracy measure in the context of fine-tuning results for a generative model?
You create a fine-tuning dedicated AI cluster to customize a foundational model with your custom training data. How many unit hours are required for fine-tuning if the cluster is active for 10 days?
How are chains traditionally created in LangChain?
Which is NOT a category of pretrained foundational models available in the OCI Generative AI service?
What is LangChain?
You create a fine-tuning dedicated AI cluster to customize a foundational model with your custom training data. How many unit hours are required for fine-tuning if the cluster is active for 10 hours?
When does a chain typically interact with memory in a run within the LangChain framework?
How does the temperature setting in a decoding algorithm influence the probability distribution over the vocabulary?
What is the purpose of the "stop sequence" parameter in the OCI Generative AI Generation models?
What is the purpose of frequency penalties in language model outputs?
What does the Loss metric indicate about a model's predictions?
Which statement is true about the "Top p" parameter of the OCI Generative AI Generation models?
What do embeddings in Large Language Models (LLMs) represent?
How can the concept of "Groundedness" differ from "Answer Relevance" in the context of Retrieval Augmented Generation (RAG)?
Which role does a "model endpoint" serve in the inference workflow of the OCI Generative AI service?
Which is a distinguishing feature of "Parameter-Efficient Fine-Tuning (PEFT)" as opposed to classic "Fine-tuning" in Large Language Model training?
In which scenario is soft prompting especially appropriate compared to other training styles?
What is the characteristic of T-Few fine-tuning for Large Language Models (LLMs)?
Accuracy in vector databases contributes to the effectiveness of Large Language Models (LLMs) by preserving a specific type of relationship. What is the nature of these relationships, and why arethey crucial for language models?
When is fine-tuning an appropriate method for customizing a Large Language Model (LLM)?