What is fine-tuning in NLP?

One of the best attributes of developing an NLP model is there are tons of pre-trained language models you can use to train your model. Unfortunately, since most NLP models are domain-specific, these generic training models aren’t always appropriate for the model’s intended purpose. That’s where fine-tuning comes in.

Also known as transfer learning, this process enables you to re-train a pre-trained model on a new dataset, thus making it more effective in its intended purpose. This article will cover everything from what fine-tuning in NLP is to the various techniques involved in the process.

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What does fine-tuning mean?

Transfer learning involves transferring ‘knowledge’ from a large pre-trained language model to another model designed to perform a specified task. This typically involves adjusting certain parameters within the pre-trained model and applying the result to the new model to make it more effective or optimize its performance.

This technique is invaluable in developing NLP models since it eliminates the need to train the models from scratch, thus saving time, computational resources, and money. So, what is fine-tuning in NLP?

What is fine-tuning in NLP?

In the context of Natural Language Processing, transfer learning is a re-training technique where large language models trained on large datasets are optimized to perform similar tasks on another dataset. [1]

Developers typically use large open-source sources to train a model, so it learns the general patterns of language. They then fine-tune the model on a smaller, task-specific dataset to enable the model to learn the specific patterns of language specific to its intended purpose.

For instance, developers may use a large, generic dataset in the English language to train a model, then utilize domain adaptation to optimize the model to a specific domain such as a law or medicine domain. [2]

Techniques used to fine-tune NLP models

There are numerous techniques involved in transfer learning in NLP, each suited to specific use cases. Here are five of the most common transfer learning techniques in NLP.

Task-specific architecture modification

Task-specific architecture modification typically involves making changes to the architecture of a pre-trained model to better fit the requirements of its intended purpose. For instance, a pre-trained model trained for text classification may not be optimized for language modeling. In this case, the developer may modify the model’s architecture to improve its performance in language modeling applications.

Domain adaptation

Domain adaptation is used in specific cases where the pre-trained model doesn’t have enough operation capability for a specific task or domain. The pre-trained model is typically fine-tuned on smaller datasets that are specific to the target domain. [3] This allows the model to acquire the knowledge it needs to improve its performance in the specific domain.

Knowledge distillation

Knowledge distillation involves transferring knowledge from a large, pre-trained model to a smaller model such that the smaller model mimics the behavior of the larger model. [4] The process typically works by training a large model on a large dataset, then using it to generate soft targets for a smaller model trained on the same dataset.

In this context, soft targets are probability distributors over the possible outputs of a pre-trained model, i.e., the probabilities that a pre-trained model assigns to different possible outputs for a given input.

During the development process, developers use these soft probabilities from the large model as targets for a smaller model that wants to mimic the attributes of the larger model. They do this by minimizing the probability of distributors produced by the large, pre-trained model and the smaller model.

This allows the smaller model to learn from the rich, subtle knowledge encoded in the large, pre-trained model’s probability distributions, thus improving the generalization performance of the smaller model.

Examples of pre-trained models used in transfer learning

Most large pre-trained models are trained on massive datasets to learn complex language patterns and structures. Some of these models come with APIs that enable you to fine-tune them for specific NLP tasks. Some of the most commonly used NLP models in transfer learning include:

BERT

BERT (Bidirectional Encoder Representations from Transformers) is a large, pre-trained model from Google capable of understanding the context of words in a sentence in relation to each other. It achieves this through a transformer-based architecture that allows it to take the context of adjoining words in a sentence. This enables it to understand complex language structures like long-range dependencies and idiomatic expressions.

BERT can be fine-tuned for numerous NLP tasks such as named entity recognition, sentiment analysis, and question answering. [5]

GPT-3

GPT-3 is a large language model trained on massive datasets through unsupervised learning techniques. With 175 billion parameters, GPT-3 is one of the largest language models on the market. The model can be fine-tuned for specific tasks such as language translation and sentiment analysis.

XLNet

XLNet is a pre-trained, transformer-based neural network. The model uses a permutation-based approach, where it considers all possible permutations of the input sequence during pre-training, thus allowing it to capture more complex relationships between different parts of the input sequence, which ultimately results in improved performance.

The model can be fine-tuned for a wide variety of NLP tasks, such as question answering, text classification, and language translation.

Read more about The best NLP model GPT alternatives

Use cases of fine-tuning in NLP

Many developers are now opting to fine-tune existing large NLP models over creating models from scratch due to the level of convenience, effectiveness, and efficiency it provides. Here are some of the most notable use cases of fine-tuning in NLP:

Text classification

Fine-tuned pre-trained models can be used for text classification applications such as topic classification, sentiment analysis, and topic classification. Take BERT, for instance. The large language model can be fine-tuned for a wide variety of text classification applications like sentiment analysis, spam filtering, and text categorization.

Named entity recognition

Naked entity recognition (NER) refers to the process of identifying and extracting named entities such as people, organizations, and locations from a given text-based dataset. [6] Models like ELMO and BERT can be fine-tuned for NER applications.

Question-answering systems

Question-answering systems have become quite popular over the past few years. These systems provide simplified answers to complex queries, thus limiting the research time needed for a specific task. Question-answering systems like GPT-3 can be fine-tuned for specific domains to increase their proficiency in answering questions in that domain.

Machine translation

Machine translation is the process of using computer software to translate text from one language to another. Large pre-trained models like BERT, RoBERTa, and GPT-3 are already proficient in machine translation tasks. However, you can still fine-tune the models in a specific language to enable them to generate more accurate results.

Sentiment analysis

Sentiment analysis is a machine learning technique used to extract subjective information from text, such as emotions, opinions, and attitudes. Large, pre-trained models like BERT and RoBERTa can be fine-tuned for sentiment analysis applications such as analyzing the sentiment of customer reviews, social media posts, and news articles.

Challenges in fine-tuning NLP Models

Transfer learning presents an efficient and effective way to adopt pre-trained models to specific domains with limited data requirements. Unfortunately, you have to address and overcome certain challenges in order to effectively fine-tune an NLP model.

Data selection and preprocessing

To effectively fine-tune an NLP model, you have to use a specific dataset as a representative of the target domain. However, selecting and preprocessing this data can be challenging since the data must be carefully curated to ensure its relevance to the task. You also have to ensure the data is of high quality.

If the data is not representative of the intended task domain, the fine-tuned model will not perform efficiently. Preprocessing is also important as it directly affects the quality of the features used by the model.

Take tokenization, for example. Tokenization is one of the most popular preprocessing techniques applied to training data. This method involves breaking down the text into individual words or subwords, which are then used as input to the model. When applied incorrectly or inconsistently, this method can drastically affect the quality of the model’s features by altering the meaning of the text.

To address these challenges, researchers and developers employ data augmentation techniques. Data augmentation involves generating new examples by applying transformations such as random deletions, insertions, or substitutions to the existing data. Ultimately, this helps improve the quality and quantity of the data, thus leading to better-performing models.

Overfitting and underfitting

Overfitting occurs when a model is trained on too much task-specific data, causing it to perform poorly on new examples that are not necessarily tied to the domain. Conversely, underfitting occurs when the model is not trained on enough task-specific data, leading to poor performance on the training and validation sets. You can overcome this challenge by using early stopping and regularization techniques. [7]

Final thoughts

Transfer learning offers an easy and cost-effective way to train new models using existing datasets, which eliminates the need to train the model from scratch. There are several techniques involved in the process, each suited to specific models and use cases. The result is often better performing re-trained models that are proficient in specific domain tasks. See our NLP solutions to find out more.

References

[1] Deeplizard.com. Deep Learning Fundamentals. URL: https://bit.ly/3Yczc0M. Accessed February 25, 2023
[2] Edoc.ub.uni. URL: https://edoc.ub.uni-muenchen.de/27831/7/Sedinkina_Marina.pdf. Accessed February 25, 2023
[3] Aclanthology.org. URL: https://aclanthology.org/2020.coling-main.603.pdf.  Accessed February 25, 2023
[4] Neptune.ai. Knowledge Distillation. URL: https://neptune.ai/blog/knowledge-distillation. Accessed February 25, 2023
[5] Tensorflow.org. Fine Tunin a BERT Model. URL: https://www.tensorflow.org/tfmodels/nlp/fine_tune_bert. Accessed February 25, 2023
[6] Veeksforgeeks.org. Named Entity Recognition. URL: https://bit.ly/3Y9Kqml. Accessed February 25, 2023.
[7] Towardsdatascience.com. Comprehensive guide to Regularization Techniques in Deep Learning. URL: https://bit.ly/3m09crM. Accessed February 25, 2023