ULMFiT (Universal Language Model Fine-tuning)
It is a transfer learning approach for Natural Language Processing (NLP) that involves pre-training a language model on a large corpus and then fine-tuning it for a specific task using a smaller dataset. The key idea is to leverage the knowledge gained during pre-training on a general language understanding task to improve performance on a target task with limited labeled data. Here's a simplified manual example to illustrate the concept of ULMFiT:
Step 1: Pre-training (General Language Model): Pre-train a language model on a large corpus with a self-supervised task. The model learns to predict the next word in a sentence given the preceding context. Let's use a small example to illustrate:
Example Corpus:
1. "The quick brown fox jumps over the lazy dog." 2. "A watched pot never boils." 3. "All that glitters is not gold."
Pre-training Task: Given the context, predict the next word in each sentence.
Example Pre-trained Model: The model is pre-trained to predict the next word, capturing general language understanding.
Step 2: Fine-tuning (Target Task): Fine-tune the pre-trained language model for a specific downstream task using a smaller labeled dataset. Let's consider a sentiment classification task as an example:
Labeled Dataset for Sentiment Classification:
1. "The movie was fantastic! I loved every moment of it." (Positive) 2. "The plot was confusing, and the acting was disappointing." (Negative) 3. "I enjoyed the book; the characters were well-developed." (Positive)
Fine-tuning Task: Fine-tune the pre-trained model to predict sentiment (positive or negative) based on the given reviews.
Fine-tuned Model: The pre-trained model is fine-tuned on the sentiment classification task, adjusting its weights to better suit the target domain.
Step 3: Inference (Prediction): Use the fine-tuned model for sentiment prediction on new, unseen data.
Example Inference: Given a new review, the fine-tuned model predicts whether it's positive or negative.
ULMFiT's key contribution lies in its ability to transfer knowledge from pre-training to specific tasks. It involves layer freezing, gradual unfreezing, and discriminative fine-tuning to adapt the model efficiently to the target domain with limited labeled data.
While this example is highly simplified, ULMFiT has been successfully applied to various NLP tasks, demonstrating improved performance compared to training models from scratch, especially when labeled data is scarce
