A. According to the length of the documents
B. Based on the presence and frequency of the user-provided keywords
C. Based on the number of images and videos contained in the documents
D. By the complexity of language used in the documents
A. It selectively updates only a fraction of the model's weights.
B. It increases the training time as compared to Vanilla fine-tuning.
C. It does not update any weights but restructures the model architecture.
D. It updates all the weights of the model uniformly.
A. To analyze the reasoning process of language
B. To generate test cases for language models
C. To monitor the performance of language models
D. To debug issues in language model outputs
A. Conversation Token Buffer Memory
B. Conversation Summary Memory
C. Conversation Buffer Memory
D. Conversation ImgeMemory
A. Because diffusion models can only produce images
B. Because text generation does not require complex models
C. Because text representation is categorical unlike images
D. Because text is not categorical
A. When there is a significant amount of labeled, task-specific data available
B. When the model requires continued pretraining on unlabeled data
C. When the model needs to be adapted to perform well in a domain on which it was not originally trained
D. When there is a need to add learnable parameters to a Large Language Model (LLM) without task-specific training
A. RetrievalQA
B. GenerativeAI
C. Text Leader
D. Chain Deployment
