Overview of ULMFiT (Universal Language Model Fine-tuning), its algorithm and examples of implementation

ULMFiT（Universal Language Model Fine-tuning）

ULMFiT (Universal Language Model Fine-tuning) was proposed by Jeremy Howard and Sebastian Ruder in 2018 to effectively use pre-trained language models in natural language processing (NLP) tasks. It is an approach for fine tuning. The approach aims to achieve high performance on a variety of NLP tasks by combining transfer learning and fine tuning at different stages of training.

The main concepts and procedures of ULMFiT are as follows

1. pre-training of language models:

The first stage of ULMFiT involves pre-training the language model on a large general text corpus (e.g., Wikipedia). In this phase, contextual representations of words and tokens are learned. This pre-trained model will be used for initialization in the target task.

2. fine-tuning stage-by-stage training:

ULMFiT performs fine tuning in stages. Specifically, it consists of the following three phases

a. Fine Tuning of Language Models:.

Pre-trained language models are fine-tuned on a corpus of the target task (e.g., a dataset related to a task such as text classification, document generation, etc.). At this stage, the model has acquired task-specific information.

b. Decompress Fine Tuning of the Model: a. Decompress the model

The lower layers of the language model (encoder layer) are decompressed and retrained. This adjusts the low-level features to be suitable for the task.

c. Fine tuning on the final target task.

Final fine tuning is performed on the target task-specific data set. At this stage, the model is making task-specific predictions.

3. data downsampling and data expansion:

ULMFiT performs down-sampling from large data sets to suppress model over-training. It also uses data expansion methods to increase the diversity of the training data.

ULMFiT will be an approach that aims to maximize the power of transfer learning by achieving high performance on relatively small data sets and low-resource languages, and will be a method that is recognized as an important advance in transfer learning in the NLP community.

Algorithms used in ULMFiT (Universal Language Model Fine-tuning)

The main algorithms of ULMFiT are described below.

1. pre-training of language models:

ULMFiT pre-trains language models (usually recurrent neural networks or transformer models) using a large corpus of text. This pre-training is done to acquire general natural language comprehension skills; at this stage, the models understand the context and learn embedded representations of words.

2. fine tuning:

After a language model has been pre-trained, the model is fine-tuned using domain-specific data sets in order to apply it to a specific task. This allows the model to learn the linguistic expressions and concepts relevant to a specific task, and during fine tuning, a classifier for the new task (e.g., classification, language generation, etc.) is usually added to the last part of the model.

3. model fine-tuning in the reverse direction:

In ULMFiT, language models are usually first trained in the normal direction (normal sentence order) and then in the reverse direction (reverse sentence order). This allows the model to have a deeper understanding of the context and to be more adaptable to different tasks.

4. discriminative fine tuning:

ULMFiT uses a technique called discriminative fine tuning to generate tokens or sentences associated with a task. In this technique, an attempt is made to optimize the probability of generation in order to generate targeted tokens for a particular task.

The idea of ULMFiT influenced later transformer-based models (BERT, GPT described in “Overview of GPT and examples of algorithms and implementations“, etc.) and is the run of approaches that emphasize the importance of transition learning and fine tuning.

Example of ULMFiT (Universal Language Model Fine-tuning) implementation

A common example of ULMFiT implementation will be to use the fastai library. Below is a simple example of implementing ULMFiT using fastai. In this example, a document classification task is considered.

First, install the fastai library and prepare a dataset. In this example, we use the IMDb movie review dataset.

# Install required libraries
!pip install fastai

# Import fastai and related libraries
from fastai.text import *

# Download and unzip the IMDb dataset
path = untar_data(URLs.IMDB)

# Data Loader Settings
bs = 48
data_lm = (TextList.from_folder(path)
           .filter_by_folder(include=['train', 'test', 'unsup'])  # Include training, testing, and unclassified data
           .split_by_rand_pct(0.1)  # 10% of the data will be in the validation set.
           .label_for_lm()  # Label for language model
           .databunch(bs=bs))

# Language model training
learn = language_model_learner(data_lm, AWD_LSTM, drop_mult=0.3)
learn.fit_one_cycle(1, 1e-2)

# Preserving the Language Model
learn.save_encoder('ft_enc')

Next, the language model is used to train the document classification model.

# Data Loader Settings
data_clas = (TextList.from_folder(path, vocab=data_lm.vocab)
             .split_by_folder(valid='test')  # Make the test set a validation set
             .label_from_folder(classes=['neg', 'pos'])  # Set the label
             .databunch(bs=bs))

# Document Classification Model Training
learn = text_classifier_learner(data_clas, AWD_LSTM, drop_mult=0.5)
learn.load_encoder('ft_enc')  # Load the weights of the language model
learn.fit_one_cycle(1, 1e-2)

# Model Evaluation
learn.save('ulmfit-model')

This code is an example of implementing ULMFiT on the IMDb dataset. First, a language model is trained, and then the trained language model is applied to the document classification model. The Fast AI library makes it easy to implement ULMFiT.

Because ULMFiT is fine-tuned with datasets relevant to the target task, it can be customized to the dataset and task settings appropriate for the actual task.

The Challenge for ULMFiT（Universal Language Model Fine-tuning)

ULMFiT (Universal Language Model Fine-tuning) is a promising approach to transfer learning, but several challenges exist. The main challenges of ULMFiT are described below.

1. Data diversity and scale:

Although ULMFiT assumes that it is trained on a large dataset, it requires a dataset related to the target task in order to apply it to a specific task. Lack of data relevant to the target task may limit the performance of fine tuning.

2. generality and domain adaptation:

Because ULMFiT transfers general language models and applies them to tasks, it can be difficult to generate models that are optimized for a specific domain or task. Applying it to a specific domain requires more powerful fine tuning with the target task data.

3. handling long sentences:

ULMFiT is well suited for processing relatively short sentences; it can be difficult to process very long sentences or texts. Methods to process long sentences by breaking them up appropriately are needed.

4. handling of polysemous words:

ULMFiT learns the contextual embedding of words, but is limited to cases where the same word has different meanings in different contexts. Dealing with polysemous words will be a challenge.

5. application to low-resource languages:

When applying ULMFiT to low-resource languages, large pre-trained models and high-quality datasets may not be available. Methods to address this are needed.

To address these challenges, it is common to optimize data collection and preprocessing for the target task and to utilize domain adaptation and data extension methods. Improved versions of ULMFiT and other transition learning approaches are also continuously being investigated, and solutions to the challenges are evolving.

Strategies to address the challenges of ULMFiT (Universal Language Model Fine-tuning)

Several measures can be considered to address the challenges of ULMFiT. Below are the main challenges of ULMFiT and countermeasures to address them.

1. dealing with the diversity and scale of data:

Transfer learning and data expansion: When there is a lack of data related to the target task, fine-tuning the language model using data from similar tasks can be considered. For more information on transfer learning, For details, please refer to “Overview of Transfer Learning, Algorithms, and Examples of Implementations. Data expansion techniques (e.g., random masking of sentences, adding random noise) can also be used to increase the diversity of the training data. For more information on data augmentation, see “Small Data Machine Learning Approaches and Examples of Various Implementations.

2. support for generality and domain adaptation:

Target domain-specific fine tuning: When the target task is related to a specific domain, it is useful to perform additional fine tuning with data specific to the target domain. This allows the model to acquire features appropriate to the target domain. For more information, see “Target Domain-Specific Fine Tuning in Machine Learning Techniques.

3. handling of long sentences:

Sentence Segmentation: Sentence segmentation is used to properly handle long sentences and phrases. Long sentences can be segmented into shorter segments and fed into the model to enable processing of longer sentences. For more details, please refer to “Sentence Segmentation for NLP Processing of Long Sentences“.

4. handling of polysemous words:

Subword Segmentation: By segmenting words into subwords, it is easier to accurately capture the meaning of polysemous words. For more information on handling polysemous words, see “Handling Polysemous Words in Machine Learning“.

5. support for low-resource languages:

Efficient use of transfer learning: When applying ULMFiT to low-resource languages, consider ways to effectively use resources, such as using small amounts of target task data for fine tuning and freezing parts of the language model. See also “Approaches to Machine Learning with Small Data and Examples of Various Implementations.

Reference Information and Reference Books

For more information on natural language processing in general, see “Natural Language Processing Technology” and “Overview of Natural Language Processing and Examples of Various Implementations.

Reference books include “Natural language processing (NLP): Unleashing the Power of Human Communication through Machine Intelligence“.

“Practical Natural Language Processing: A Comprehensive Guide to Building Real-World NLP Systems“

“Natural Language Processing With Transformers: Building Language Applications With Hugging Face“