What's the advantage of using transformer-based vs LSTM-based in NLP?

quangngoc

To create sequence-to-sequence models before the Transformer, we used the famous LSTM with its Encoder-Decoder architecture, where

The "Encoder" part that creates a vector representation of a sequence of words.
The "Decoder" returns a sequence of words from the vector representation.

The LSTM model takes into account the interdependence of words, so we need inputs of the previous state to make any operations on the current state. This model has a limitation: it is relatively slow to train and the input sequence can't be passed in parallel.

Now, the idea of the Transformer is to maintain the interdependence of the words in a sequence without using a recurrent network but only the attention mechanism that is at the center of its architecture. The attention measures how closely two elements of two sequences are related.

In transformer-based architectures, the attention mechanism is applied to a single sequence (also known as a self-attention layer). The self-attention layer determines the interdependence of different words in the same sequence, to associate a relevant representation with it. Take for example the sentence: "The dog didn't cross the street because it was too tired". It is obvious to a human being that "it" refers to the "dog" and not to the "street". The objective of the self-attention process will therefore be to detect the link between "dog" and "it". This feature makes transformers much faster to train compared to their predecessors, and they have been proven to be more robust against noisy and missing data.

As a plus, in contextual embeddings, transformers can draw information from the context to correct missing or noisy data and that is something that other neural networks couldn’t offer.

gpt4

Transformer-based models offer several significant advantages over LSTM-based models for various NLP tasks. Below are some of the key benefits:

Handling of Long-range Dependencies:
Transformers use self-attention mechanisms, which allow them to consider all parts of the input sequence simultaneously. This ability enables them to capture long-range dependencies in the input data more effectively than LSTMs, which must process sequences step by step and may struggle with dependencies that span over long distances due to issues like vanishing gradients.
Parallelization and Training Speed:
Transformers process entire sequences of data in parallel rather than sequentially, as is the case with LSTM models. This parallel processing makes transformers more suited for training on modern hardware (like GPUs and TPUs), leading to much faster training times compared to the inherently sequential nature of LSTMs.
Scalability:
The architecture of transformers is more scalable to large amounts of data compared to LSTM-based models. They are often trained on massive corpora and can tap into vast amounts of information to improve their language understanding, making them excel at transfer learning tasks as well.
Flexibility and Generalization:
Transformer-based models, especially when pre-trained on a large corpus, have shown remarkable flexibility in adapting to a wide range of tasks with minimal task-specific parameter tuning. They have been effective in generalizing their pre-trained knowledge to a variety of NLP tasks through fine-tuning.
Better Gradient Flow:
Due to the absence of recurrent connections and the use of residual connections with layer normalization, transformers tend to have better gradient flow during backpropagation, which aids in the training of deeper models without running into vanishing or exploding gradient problems that are common in LSTM architectures.
Modeling of Bidirectional Contexts:
Models like BERT (Bidirectional Encoder Representations from Transformers), which are based on the transformer architecture, can simultaneously capture bidirectional contexts by using a masked language model pre-training objective. This allows for a deeper understanding of language context, which isn't as straightforward in unidirectional or even bidirectional LSTMs.
Advances in Model Size and Capabilities:
Since transformers lend themselves to more effective parallelization, researchers have been able to scale them up to create very large models like GPT-3, T5, and others. The scale of these models has resulted in significant improvements in performance across a range of language tasks and a movement towards more powerful few-shot and zero-shot learning capabilities.

Despite these advantages, there are still scenarios where LSTM-based models can be useful, particularly when working with smaller datasets or when model interpretability and operational resource constraints are a priority. However, in the current landscape of NLP, transformer-based models are widely regarded as the leading approach for high performance across a broad range of language understanding tasks.