Why QA systems extract answers in NLP - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to extract the answer from the model's output.

NLP

answer = model.predict(question, context).[1]()

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

Adecode

Bget_answer

Cstrip

Dextract

Attempts:

3 left

💡 Hint

Common Mistakes

Using methods that do not convert tokens to text, like 'extract' or 'get_answer'.

The decode() method converts the model's output tokens into a readable string answer.

2fill in blank

medium

Complete the code to tokenize the input question for the QA system.

NLP

inputs = tokenizer.[1](question, return_tensors='pt')

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

Atransform

Btokenize

Cparse

Dencode

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'tokenize' which returns tokens but not tensor format.

The encode() method converts the question text into tokens the model can understand.

3fill in blank

hard

Fix the error in the code to get the start position of the answer.

NLP

start_pos = outputs.start_logits.[1](dim=1).argmax()

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

Asum

Bmax

Csoftmax

Dmean

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'max' directly on logits without softmax.

The softmax() function converts logits to probabilities before finding the max index.

4fill in blank

hard

Fill both blanks to extract the answer text from tokens.

NLP

answer_tokens = inputs.input_ids[0][[1]:[2]]
answer = tokenizer.decode(answer_tokens)

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

Astart_pos

Bend_pos

C0

Dlen(inputs.input_ids[0])

Attempts:

3 left

💡 Hint

Common Mistakes

Using fixed indices like 0 or full length instead of predicted positions.

We slice tokens from start_pos to end_pos to get the answer span.

5fill in blank

hard

Fill all three blanks to prepare inputs and get the answer from the QA model.

NLP

inputs = tokenizer.[1](question, context, return_tensors='pt')
outputs = model(**inputs)
start_pos = outputs.start_logits.[2](dim=1).argmax()
end_pos = outputs.end_logits.[3](dim=1).argmax()

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

Aencode

Bsoftmax

Dtokenize

Attempts:

3 left

💡 Hint

Common Mistakes

Skipping softmax or using tokenize instead of encode.

We encode inputs, then apply softmax to logits before argmax to find answer positions.

Practice

1. Why do Question Answering (QA) systems extract answers from text?

easy

A. To provide quick and exact information to users

B. To generate random text for entertainment

C. To translate text into another language

D. To summarize long documents without details

Solution

1. Step 1: Understand the purpose of QA systems

   QA systems are designed to find specific answers from a given text to help users quickly.

2. Step 2: Compare options with QA system goals

   Only To provide quick and exact information to users matches the goal of providing quick and exact information, while others describe unrelated tasks.

3. Final Answer:

   To provide quick and exact information to users -> Option A

4. Quick Check:

   QA systems extract answers = quick, exact info [OK]

Hint: QA systems aim to give precise answers fast [OK]

Common Mistakes:

• Confusing QA with translation or summarization
• Thinking QA generates random text
• Assuming QA only summarizes documents

2. Which of the following is the correct way to use a QA system in code to get an answer?

easy

A. Provide multiple unrelated documents without specifying a question

B. Provide a question and context text, then call the QA model to extract the answer

C. Only provide a question without any context to get an answer

D. Input random numbers to the QA model to get an answer

Solution

1. Step 1: Recall how QA systems work

   QA systems need both a question and a context (text) to find the correct answer.

2. Step 2: Evaluate each option

   Only Provide a question and context text, then call the QA model to extract the answer correctly describes providing question and context to extract an answer; others miss key inputs or are irrelevant.

3. Final Answer:

   Provide a question and context text, then call the QA model to extract the answer -> Option B

4. Quick Check:

   QA usage = question + context [OK]

Hint: QA needs both question and context to work [OK]

Common Mistakes:

• Trying to get answers without context
• Providing unrelated documents without a question
• Using random inputs instead of text

3. Given this Python snippet using a QA model:

question = "What color is the sky?"
context = "The sky is blue during the day and black at night."
answer = qa_model(question=question, context=context)
print(answer)

What is the expected output?

medium

A. "night"

B. "black"

C. "blue"

D. "day"

Solution

1. Step 1: Understand the question and context

   The question asks for the sky's color, and the context says "The sky is blue during the day and black at night."

2. Step 2: Identify the correct answer from context

   The model should extract "blue" as the color of the sky (the direct answer to the question).

3. Final Answer:

   "blue" -> Option C

4. Quick Check:

   Sky color = blue [OK]

Hint: Match question keywords to context for answer [OK]

Common Mistakes:

• Choosing 'black' because it appears in context
• Confusing time of day with color
• Picking unrelated words from context

4. You run a QA system but it returns an empty answer. Which of these is the most likely cause?

medium

A. The QA system always returns empty answers

B. The QA model was given both question and context correctly

C. The context contains the exact answer

D. The question is not related to the provided context

Solution

1. Step 1: Analyze why QA systems return empty answers

   If the question does not match the context, the system cannot find an answer and returns empty.

2. Step 2: Evaluate options for likely cause

   The question is not related to the provided context correctly identifies mismatch as cause; others are incorrect or unrealistic.

3. Final Answer:

   The question is not related to the provided context -> Option D

4. Quick Check:

   Unrelated question = empty answer [OK]

Hint: Check if question matches context content [OK]

Common Mistakes:

• Assuming model always fails
• Ignoring question-context relevance
• Thinking empty answer means error

5. In a customer support QA system, why is extracting exact answers from product manuals better than just summarizing the manuals?

hard

A. Because customers want quick, precise answers, not long summaries

B. Because summaries always contain errors

C. Because extracting answers is faster than reading manuals

D. Because summaries cannot be generated automatically

Solution

1. Step 1: Understand customer needs in support

   Customers usually want quick, exact answers to their questions rather than long summaries.

2. Step 2: Compare answer extraction vs summarization

   Extracting exact answers targets specific questions, while summaries provide general info, which may be less helpful.

3. Final Answer:

   Because customers want quick, precise answers, not long summaries -> Option A

4. Quick Check:

   Customer support needs precise answers [OK]

Hint: Exact answers save time over summaries [OK]

Common Mistakes:

• Thinking summaries are always error-prone
• Assuming summaries can't be automated
• Confusing speed with accuracy