Prueba AI

Which statement is NOT true about StreamlitChatMessageHistory?. A given StreamlitChatMessageHistory will NOT be persisted. A given StreamlitChatMessageHistory will not be shared across user sessions. StreamlitChatMessageHistory will store messages in Streamlit session state at the specified key. StreamlitChatMessageHistory can be used in any type of LLM application.

Why is it challenging to apply diffusion models to text generation?. Because diffusion models can only produce images. Because text is not categorical. Because text representation is categorical unlike images. Because text generation does not require complex models.

When is fine-tuning an appropriate method for customizing a Large Language Model (LLM)?. When the LLM already understands the topics necessary for text generation. When the LLM requires access to the latest data for generating outputs. When the LLM does not perform well on a task and the data for prompt engineering is too large. When you want to optimize the model without any instructions.

How does the structure of vector databases differ from traditional relational databases?. It is based on distances and similarities in a vector space. A vector database stores data in a linear or tabular format. It is not optimized for high-dimensional spaces. It uses simple row-based data storage.

How are documents usually evaluated in the simplest form of keyword-based search?. By the complexity of language used in the documents. According to the length of the documents. Based on the number of images and videos contained in the documents. Based on the presence and frequency of the user-provided keywords.

What is the purpose of Retrieval Augmented Generation (RAG) in text generation?. To store text in an external database without using it for generation. To generate text based only on the model’s internal knowledge without external data. To generate text using extra information obtained from an external data source. To retrieve text from an external source and present it without any modifications.

What does a cosine distance of 0 indicate about the relationship between two embeddings?. They are similar in direction. They are completely dissimilar. They have the same magnitude. They are unrelated.

How does the temperature setting in a decoding algorithm influence the probability distribution over the vocabulary?. Decreasing the temperature broadens the distribution, making less likely words more probable. Temperature has no effect on probability distribution; it only changes the speed of decoding. Increasing the temperature removes the impact of the most likely word. Increasing the temperature flattens the distribution, allowing for more varied word choices.

Which statement is true about string prompt templates and their capability regarding variables?. They can only support a single variable at a time. They are unable to use any variables. They support any number of variables, including the possibility of having none. They require a minimum of two variables to function properly.

How does a presence penalty function in language model generation?. It penalizes a token each time it appears after the first occurrence. It penalizes all tokens equally, regardless of how often they have appeared. It applies a penalty only if the token has appeared more than twice. It penalizes only tokens that have never appeared in the text before.

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 are they crucial for language models?. Hierarchical relationships; important for structuring database queries. Semantic relationships; crucial for understanding context and generating precise language. Temporal relationships; necessary for predicting future linguistic trends. Linear relationships; they simplify the modeling process.

What does the RAG Sequence model do in the context of generating a response?. It retrieves a single relevant document for the entire input query and generates a response based on that alone. It retrieves relevant documents only for the initial part of the query and ignores the rest. For each input query, it retrieves a set of relevant documents and considers them together to generate a cohesive response. It modifies the input query before retrieving relevant documents to ensure a diverse response.

What does the Loss metric indicate about a model’s predictions?. Loss is a measure that indicates how wrong the model’s predictions are. Loss describes the accuracy of the right predictions rather than the incorrect ones. Loss measures the total number of predictions made by a model. Loss indicates how good a prediction is, and it should increase as the model improves.

In which scenario is soft prompting appropriate compared to other training styles?. When the model needs to be adapted to perform well in a domain on which it was not originally trained. When the model requires continued pretraining on unlabeled data. When there is a need to add learnable parameters to a Large Language Model (LLM) without task-specific training. When there is a significant amount of labeled, task-specific data available.

Which statement is true about Fine-tuning and Parameter-Efficient Fine-Tuning (PEFT)?. PEFT requires replacing the entire model architecture with a new one designed specifically for the new task, making it significantly more data-intensive than Fine-tuning. Fine-tuning and PEFT do not involve model modification; they differ only in the type of data used for training, with Fine-tuning requiring labeled data and PEFT using unlabeled data. Both Fine-tuning and PEFT require the model to be trained from scratch on new data, making them equally data and computationally intensive. Fine-tuning requires training the entire model on new data, often leading to substantial computational costs, whereas PEFT involves updating only a small subset of parameters, minimizing computational requirements and data needs.

Which LangChain component is responsible for generating the linguistic output in a chatbot system?. LangChain Application. LLMs. Vector Stores. Document Loaders.

What is LangChain?. A Ruby library for text generation. A Java library for text summarization. A JavaScript library for natural language processing. A Python library for building applications with Large Language Models.

What do prompt templates use for templating in language model applications?. Python’s list comprehension syntax. Python’s str.format syntax. Python’s lambda functions. Python’s class and object structures.

When does a chain typically interact with memory in a run within the LangChain framework?. Before user input and after chain execution. Continuously throughout the entire chain execution process. Only after the output has been generated. After user input but before chain execution, and again after core logic but before output.

In the simplified workflow for managing and querying vector data, what is the role of indexing?. To map vectors to a data structure for faster searching, enabling efficient retrieval. To compress vector data for minimized storage usage. To convert vectors into a nonindexed format for easier retrieval. To categorize vectors based on their originating data type (text, images, audio).

What is the purpose of Retrievers in LangChain?. To break down complex tasks into smaller steps. To combine multiple components into a single pipeline. To retrieve relevant information from knowledge bases. To train Large Language Models.

Which is a characteristic of T-Few fine-tuning for Large Language Models (LLMs)?. It updates all the weights of the model uniformly. It selectively updates only a fraction of the model’s weights. It does not update any weights but restructures the model architecture. It increases the training time as compared to Vanilla fine-tuning.

How can the concept of “Groundedness” differ from “Answer Relevance” in the context of Retrieval Augmented Generation (RAG)?. Groundedness focuses on data integrity, whereas Answer Relevance emphasizes lexical diversity. Groundedness pertains to factual correctness, whereas Answer Relevance concerns query relevance. Groundedness refers to contextual alignment, whereas Answer Relevance deals with syntactic accuracy. Groundedness measures relevance to the user query, whereas Answer Relevance evaluates data integrity.

In the context of generating text with a Large Language Model (LLM), what does the process of greedy decoding entail?. Using a weighted random selection based on a modulated distribution. Picking a word based on its position in a sentence structure. Choosing the word with the highest probability at each step of decoding. Selecting a random word from the entire vocabulary at each step.

What does accuracy measure in the context of fine-tuning results for a generative model?. The number of predictions a model makes, regardless of whether they are correct or incorrect. How many predictions the model made correctly out of all the predictions in an evaluation. The proportion of incorrect predictions made by the model during an evaluation. The depth of the neural network layers used in the model.