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🤖 GPT

Nlp Job Interview Pretrainer

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This prompt acts as an expert NLP interviewer, generating multiple choice questions on Transformer models and search algorithms. It provides detailed explanations for both correct and incorrect answers, and helps you identify your strengths and weaknesses.
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After purchasing, you will gain access to the prompt file which you can use with GPT or the app builder. You'll receive 20 free generation credits with this purchase. By purchasing this prompt, you agree to our terms of service.
9 months ago

Prompt Details

Model
Chat - GPT-3.5 (gpt-3.5-turbo)
Token size
161 ($0.00030 / call)
Example input
[DIFFICULTY LEVEL] : ADVANCED EASY MID-LEVEL ADVANCED
Example output
Let's get started with the first question: Question 1: Transformer models like BERT and RoBERTa are designed for which type of natural language processing task? A) Speech recognition B) Image classification C) Text generation D) Sentiment analysis Please select your answer. A Question 1 - Answer Review: Your answer is A) Speech recognition. I'm sorry, but that's incorrect. Let me explain the correct answer and why the other options are not correct: Correct Answer: C) Text generation Explanation: Transformer models like BERT and RoBERTa are primarily designed for natural language processing tasks involving text, such as text classification, question answering, named entity recognition, and text generation. While they excel at understanding and generating human language, they are not specifically designed for speech recognition. Reasons to Discount Other Choices: - B) Image classification: Transformer models are not designed for image classification tasks; they are tailored to handle sequential data like text. - D) Sentiment analysis: While transformer models can perform sentiment analysis, it's not their sole or primary purpose. They are more versatile and can tackle a wide range of NLP tasks beyond sentiment analysis. Let's move on to the next question: Question 2: Which search algorithm focuses on exploring neighbors of nodes before backtracking? A) Breadth-First Search (BFS) B) Depth-First Search (DFS) C) A* Search D) Greedy Best-First Search D Question 2 - Answer Review: Your answer is D) Greedy Best-First Search. I'm sorry, but that's incorrect. Let me explain the correct answer and why the other options are not correct: Correct Answer: A) Breadth-First Search (BFS) Explanation: Breadth-First Search (BFS) is the search algorithm that explores all the neighbors of a node before moving on to their child nodes. It gradually explores the graph in layers, starting from the source node and moving outward. This ensures that nodes at the same level are visited before moving deeper into the graph. Reasons to Discount Other Choices: - B) Depth-First Search (DFS): DFS explores as far as possible along a branch before backtracking. It doesn't focus on exploring neighbors before backtracking like BFS does. - C) A* Search: A* Search is an informed search algorithm that uses heuristics to guide its search. It's not specifically focused on exploring neighbors before backtracking. - D) Greedy Best-First Search: Greedy Best-First Search prioritizes nodes that appear to be closest to the goal based on a heuristic. It's not guaranteed to explore neighbors before backtracking like BFS. Let's proceed to the next question: Question 3: Which of the following is a variant of BERT that uses dynamic masking instead of static masking during pre-training? A) XLNet B) GPT-2 C) ELMo D) Transformer-XL
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