Custom QA model fine-tuning in NLP - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is fine-tuning in the context of a custom QA model?

Fine-tuning means taking a pre-trained language model and training it a bit more on your specific question-answer data so it learns to answer questions better for your topic.

beginner

Why do we use pre-trained models for fine-tuning instead of training from scratch?

Pre-trained models already know language basics from lots of text, so fine-tuning them on your data is faster, needs less data, and gives better results than starting fresh.

beginner

What kind of data is needed to fine-tune a custom QA model?

You need pairs of questions and their correct answers, often with the context text where the answer is found, so the model learns to find and give the right answers.

intermediate

How do we measure if a custom QA model is improving during fine-tuning?

We check metrics like accuracy or F1 score on a test set of questions and answers to see if the model gives more correct answers after training.

beginner

What is the role of the context passage in a QA fine-tuning dataset?

The context passage is the text where the answer is found. The model learns to read this passage and pick the right answer to the question from it.

What does fine-tuning a QA model usually require?

A Random text documents

B Only a list of questions

C Only a list of answers

D A dataset of questions, answers, and context passages

Fine-tuning needs question-answer pairs with context so the model learns where to find answers.

Why is fine-tuning faster than training a QA model from scratch?

A Because it uses less data

B Because it skips evaluation

C Because the model already knows language patterns from pre-training

D Because it uses simpler algorithms

Pre-trained models have learned language basics, so fine-tuning adjusts them quickly for your task.

Which metric is commonly used to evaluate QA model performance?

A Mean squared error

B F1 score

C BLEU score

D Perplexity

F1 score measures how well the model finds correct answers balancing precision and recall.

What is the main goal of fine-tuning a QA model?

A To make the model answer questions accurately for your specific data

B To increase the model size

C To reduce the number of questions

D To generate new questions

Fine-tuning adapts the model to your data so it answers questions correctly.

Which part of the QA dataset helps the model locate the answer?

A Context passage

B Question only

C Answer only

D Model architecture

The context passage contains the information where the answer is found.

Explain the process and purpose of fine-tuning a custom QA model.

Think about how a general language model learns to answer your specific questions better.

Describe the types of data and evaluation metrics used in fine-tuning a QA model.

Consider what the model needs to learn and how we check if it learned well.