Why text generation creates content in NLP - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to generate text using a simple model.

NLP

generated_text = model.[1](input_sequence)

Drag options to blanks, or click blank then click option'

Apredict

Bgenerate

Cfit

Dcompile

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'fit' instead of 'predict' which trains the model rather than generating output.

Using 'compile' which prepares the model but does not generate output.

The predict method is used to generate output from the model given an input sequence.

2fill in blank

medium

Complete the code to prepare input text for the model.

NLP

input_sequence = tokenizer.[1](raw_text)

Drag options to blanks, or click blank then click option'

Adecode

Bfit

Cencode

Dtransform

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'decode' which converts tokens back to text.

Using 'fit' which is for training the tokenizer.

The encode method converts raw text into tokens the model can understand.

3fill in blank

hard

Fix the error in the code to generate text from the model.

NLP

output = model.predict([1])

Drag options to blanks, or click blank then click option'

Araw_text

Binput_sequence

Ctokenizer

Dmodel

Attempts:

3 left

💡 Hint

Common Mistakes

Passing raw text directly causes errors.

Passing the tokenizer or model object instead of input data.

The model expects tokenized input, so we must pass input_sequence, not raw text.

4fill in blank

hard

Fill both blanks to convert model output tokens back to readable text.

NLP

decoded_text = tokenizer.[1](output_tokens, [2]=True)

Drag options to blanks, or click blank then click option'

Adecode

Bencode

Cskip_special_tokens

Dadd_special_tokens

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'encode' instead of 'decode' reverses the process.

Not skipping special tokens results in extra symbols in output.

Use decode to convert tokens to text and skip_special_tokens=True to remove special tokens.

5fill in blank

hard

Fill all three blanks to generate text and decode it properly.

NLP

input_seq = tokenizer.[1](text)
output_tokens = model.[2](input_seq)
result = tokenizer.[3](output_tokens, skip_special_tokens=True)

Drag options to blanks, or click blank then click option'

Aencode

Bpredict

Cdecode

Dfit

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'fit' instead of 'encode' or 'predict'.

Decoding before prediction causes errors.

First encode the text, then predict output tokens, finally decode tokens to text.

Practice

1. What is the main reason text generation models create new content?

easy

A. They predict the next word based on previous words

B. They copy sentences from a fixed list

C. They randomly select words without context

D. They translate text from one language to another

Solution

1. Step 1: Understand how text generation works

   Text generation models use previous words to predict the next word, creating new sentences.

2. Step 2: Compare options with this understanding

   Only They predict the next word based on previous words describes this process correctly; others describe unrelated or incorrect methods.

3. Final Answer:

   They predict the next word based on previous words -> Option A

4. Quick Check:

   Next word prediction = C [OK]

Hint: Text generation predicts next words, not copy or random picks [OK]

Common Mistakes:

• Thinking text is copied from a list
• Believing words are chosen randomly
• Confusing generation with translation

2. Which of the following is the correct way to start generating text using a model like GPT-2?

easy

A. model.train(start_text)

B. model.generate(start_text)

C. model.predict_label(start_text)

D. model.translate(start_text)

Solution

1. Step 1: Identify the function for text generation

   Text generation uses a method like generate to produce new text from a start.

2. Step 2: Eliminate unrelated functions

   train is for learning, predict_label is for classification, and translate is for language translation.

3. Final Answer:

   model.generate(start_text) -> Option B

4. Quick Check:

   Text generation method = generate [OK]

Hint: Use generate() to create text, not train() or translate() [OK]

Common Mistakes:

• Confusing training with generating
• Using classification methods for generation
• Mixing translation with generation

3. Given this Python code using a text generation model:

start_text = 'Once upon a time'
output = model.generate(start_text, max_length=10)
print(output)

What is the expected output type?

medium

A. A list of numbers representing word indexes

B. An error because max_length is invalid

C. A string containing a sentence starting with 'Once upon a time'

D. A boolean indicating success or failure

Solution

1. Step 1: Understand the generate function output

   The generate function returns generated text as a string starting with the input.

2. Step 2: Analyze the code snippet

   It prints the output, which should be a string sentence starting with 'Once upon a time'.

3. Final Answer:

   A string containing a sentence starting with 'Once upon a time' -> Option C

4. Quick Check:

   Output type = string sentence [OK]

Hint: Generate outputs text strings, not lists or booleans [OK]

Common Mistakes:

• Expecting numeric lists instead of text
• Assuming max_length causes errors
• Thinking output is a success flag

4. This code tries to generate text but raises an error:

start = 'Hello'
output = model.generate(start, max_len=20)
print(output)

What is the likely cause of the error?

medium

A. The parameter name should be max_length, not max_len

B. The start text must be a list, not a string

C. The model.generate method does not exist

D. The print statement is missing parentheses

Solution

1. Step 1: Check parameter names for generate()

   The correct parameter to limit output length is max_length, not max_len.

2. Step 2: Verify other code parts

   Start text as string is valid, model.generate exists, and print uses parentheses correctly.

3. Final Answer:

   The parameter name should be max_length, not max_len -> Option A

4. Quick Check:

   Correct param name = max_length [OK]

Hint: Use exact parameter names like max_length to avoid errors [OK]

Common Mistakes:

• Using wrong parameter names
• Thinking input must be a list
• Ignoring Python 3 print syntax

5. You want to generate a story summary using a text generation model. Which approach best explains why the model creates new content rather than copying existing text?

hard

A. The model translates the original story into another language and back

B. The model searches a database for exact matching summaries and returns them

C. The model randomly selects words from a dictionary without context

D. The model predicts each next word based on learned patterns, creating unique sentences

Solution

1. Step 1: Understand text generation for summaries

   Models generate summaries by predicting next words using learned language patterns, not copying exact text.

2. Step 2: Evaluate options based on this understanding

   Only The model predicts each next word based on learned patterns, creating unique sentences describes this predictive generation; others describe copying, random selection, or translation.

3. Final Answer:

   The model predicts each next word based on learned patterns, creating unique sentences -> Option D

4. Quick Check:

   Generation = prediction of next words [OK]

Hint: Generation predicts words, it doesn't copy or translate [OK]

Common Mistakes:

• Thinking generation copies exact text
• Confusing generation with translation
• Assuming random word selection