Why Transformers Are the Future: Limitations of LSTMs and How They’re Solved

Recurrent Neural Networks (RNNs) and their variants like Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) models have significantly improved sequence modeling tasks such as text generation, machine translation, and speech recognition. However, despite their advancements, LSTMs face several limitations that hinder their scalability and effectiveness, especially in handling long-range dependencies. To address these shortcomings, a new architecture, Transformers, was introduced, revolutionizing sequence modeling with its efficiency and performance.

In this article, we will explore the key limitations of LSTMs and how the Transformer architecture addresses these issues, making it the dominant model for natural language processing (NLP) tasks today. We’ll also dive into a simplified explanation of how Transformers work, using a practical example.

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Limitations of LSTMs

1. Sequential Nature and Inefficiency

LSTMs process sequences step by step, which inherently limits their ability to parallelize computations. This sequential nature leads to slower training times, especially with longer sequences.

2. Difficulty with Long-Range Dependencies

Although LSTMs are designed to capture long-range dependencies in sequences, they are not always effective at doing so. As sequences grow longer, LSTMs struggle to retain relevant information from earlier time steps due to gradient decay (vanishing gradient problem).

3. Memory Constraints

LSTMs must maintain hidden states and store past information over time, leading to memory limitations.

4. Limited Parallelization

LSTMs depend on previous time steps to calculate the current one, making it impossible to parallelize across different sequence steps.

5. Exploding and Vanishing Gradients

LSTMs still face exploding and vanishing gradient problems during training, making optimization challenging.

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Enter Transformers: Overcoming LSTM Limitations

The Transformer architecture, introduced in the paper “Attention is All You Need” (2017), revolutionized sequence modeling by solving many of the issues plaguing LSTMs. Unlike LSTMs, Transformers do not rely on the sequential processing of data, allowing them to be more efficient and scalable.

Key Innovations of the Transformer Architecture:

1. Parallelization through Self-Attention: Transformers process the entire sequence at once, allowing for faster computation through parallelization.
2. Handling Long-Range Dependencies: Transformers excel at modeling long-range dependencies using self-attention, attending directly to relevant information at any position in the sequence.
3. Scalability: Transformers scale efficiently to very large datasets and longer sequences, making them ideal for complex NLP tasks.
4. Positional Encoding: Since Transformers process sequences in parallel, they rely on positional encodings to capture the order of tokens in the sequence.
5. Better Gradient Flow: Transformers minimize gradient-related issues, improving model optimization.

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How Transformers Work

Now, let’s break down how a Transformer works in a simplified way:

Transformers solve sequence modeling tasks using self-attention instead of processing tokens one by one like LSTMs. At the heart of the Transformer is the concept of attention, which allows the model to “attend” to different parts of the input sequence to find relationships between words, regardless of their distance.

Imagine this scenario:

You are reading a book, and each word in a sentence helps you understand the meaning of other words. For example, in the sentence, “The cat sat on the mat,” the word “cat” gives you context for the word “sat.” Similarly, the word “mat” gives you an idea of where the cat is. This is exactly what self-attention does—it lets the model consider every word in the sentence when processing each word.

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Transformer Architecture Components

1. Self-Attention Mechanism
   • Each word (or token) in the input sequence can attend to every other word. For example, when processing the word “cat” in the sentence “The cat sat on the mat,” the model can also look at “sat” and “mat” to understand the full context.
2. Positional Encoding
   • Since Transformers process the whole sequence at once, they need a way to know the position of each word. Positional encoding is like adding a “position tag” to each word so the model can differentiate between “the cat” and “cat the.”
3. Feedforward Layers
   • After calculating attention, each word’s information passes through regular feedforward neural layers to refine its understanding.
4. Multi-Head Attention
   • Rather than calculating attention in just one way, the model calculates it multiple times from different perspectives (heads). This allows the model to understand the relationships between words in more nuanced ways.
5. Encoder-Decoder Architecture
   • In tasks like translation, the Transformer uses two parts: the encoder, which reads and understands the input text, and the decoder, which generates the output text (e.g., in another language).

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From where to Get / download Pre-trained Transformer Models

Pre-trained Transformer models have become widely accessible and easy to integrate into your projects, saving you the time and computational resources required for training from scratch. Here are some popular sources for pre-trained Transformer models:

1. Hugging Face Model Hub

• Hugging Face provides an extensive repository of pre-trained Transformer models for tasks such as text generation, translation, and sentiment analysis. Models like GPT-2, GPT-3, BERT, and RoBERTa are readily available.
• Website: https://huggingface.co/models

Example of how to load a model from Hugging Face:

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("facebook/bart-large")
model = AutoModelForSeq2SeqLM.from_pretrained("facebook/bart-large")

2. TensorFlow Hub

• TensorFlow Hub offers various pre-trained models, including Transformers, which can be integrated into TensorFlow projects.
• Website: https://tfhub.dev

3. OpenAI API

• OpenAI offers powerful Transformer-based models such as GPT-3, which you can access through their API for various NLP tasks, including text generation.
• Website: https://beta.openai.com/

4. Google Cloud AI Hub

• Google Cloud’s AI Hub provides pre-trained models, including Transformer models, for cloud-based machine learning solutions.
• Website: https://cloud.google.com/vertex-ai/

5. AWS Sagemaker

• Amazon Web Services (AWS) SageMaker offers pre-trained Transformer models as part of their managed machine learning services.
• Website: https://aws.amazon.com/bedrock/

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Example: Transformer in Action

Let’s consider a practical example where we use a Pretrained Transformer-based model like GPT-2 to generate text:

Ensure you have installed transformers: pip install transformers

1. GPT-2: (Generative Pre-trained Transformer 2)

GPT-2 is an autoregressive language model that generates text by predicting the next word in a sequence.

from pprint import pprint
from transformers import GPT2Tokenizer, GPT2LMHeadModel

# Load pre-trained GPT-2 model and tokenizer
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2LMHeadModel.from_pretrained('gpt2')

# Set padding token ID (GPT-2 doesn't have a padding token by default)
tokenizer.pad_token = tokenizer.eos_token

# Seed text for text generation
seed_text = "Once upon a time"

# Tokenize the input text and return attention mask
inputs = tokenizer.encode_plus(seed_text, return_tensors='pt', padding=True, truncation=True, max_length=50)

# Generate text with attention mask
outputs = model.generate(inputs['input_ids'], 
                         attention_mask=inputs['attention_mask'], 
                         max_length=50, 
                         num_return_sequences=1, 
                         pad_token_id=tokenizer.eos_token_id)

# Decode the output
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
pprint(generated_text)

Code Explanation:

1. Loading the Pre-trained Model: We use GPT-2, a pre-trained Transformer model, which is designed for text generation. This model has already learned patterns of how sentences are constructed from large datasets.
2. Tokenization: The input text (“Once upon a time”) is converted into numerical tokens (numbers) that the model understands.
3. Text Generation: The model predicts the next words in the sequence based on the input and continues to do so until it reaches the maximum length of 50 tokens.
4. Decoding the Output: The generated tokens are converted back into human-readable text and printed.

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2. BERT: (Bidirectional Encoder Representations from Transformers)

BERT is not typically used for text generation but for masked language modeling (filling in missing words).

from transformers import BertTokenizer, BertForMaskedLM
import torch

# Load BERT model and tokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertForMaskedLM.from_pretrained('bert-base-uncased')

# Input text with a masked token
text = "Artificial intelligence is the [MASK] of the future."
inputs = tokenizer(text, return_tensors='pt')

# Predict the masked word
with torch.no_grad():
    outputs = model(**inputs)
    predictions = outputs.logits

# Decode the predicted token
masked_index = inputs['input_ids'][0].tolist().index(tokenizer.mask_token_id)
predicted_token_id = predictions[0, masked_index].argmax(dim=-1).item()
predicted_token = tokenizer.decode([predicted_token_id])

print(f"BERT Predicted Text: {text.replace('[MASK]', predicted_token)}")

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3. T5: (Text-to-Text Transfer Transformer)

T5 can handle a wide variety of tasks, including text generation. It treats every task as a text-to-text problem. this T5 requires SentencePiece library, you can install using : pip install SentencePiece

from transformers import T5Tokenizer, T5ForConditionalGeneration

# Load T5 model and tokenizer
tokenizer = T5Tokenizer.from_pretrained('t5-small')
model = T5ForConditionalGeneration.from_pretrained('t5-small')

# Input prompt for text generation
input_text = "Translate English to French: The weather is beautiful today."

# Tokenize and encode the input text
inputs = tokenizer(input_text, return_tensors='pt')

# Generate text
outputs = model.generate(inputs['input_ids'], max_length=50)

# Decode the generated text
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(f"T5 Generated Text:\n{generated_text}")

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4. Find Pre-trained Models Available via Hugging Face

You can easily access a wide variety of pre-trained models for text generation through Hugging Face’s Model Hub. These include not just GPT-2, T5, and BART, but many other state-of-the-art models.

Visit: https://huggingface.co/models

Here are the steps to load a different pre-trained model:

1. Search for the model you want (e.g., GPT-3, BERT, etc.).
2. Use the transformers library to load that model using its name

from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained('model_name')
model = AutoModelForCausalLM.from_pretrained('model_name')

Replace model_name with the actual name of the model from the Model Hub.

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Explanation of Parameters in Code

• max_length: Defines the maximum number of tokens in the generated sequence.
• num_return_sequences: Specifies how many different generated sequences you want.
• skip_special_tokens=True: Ensures that special tokens like <pad> or <eos> are not included in the final output.
• pad_token_id=tokenizer.eos_token_id: For GPT-2, which doesn’t have a dedicated padding token, we set the padding token to be the same as the end-of-sequence token.

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Using these different models, you can compare how they handle text generation tasks. Each model architecture has unique strengths and is suitable for various kinds of natural language processing (NLP) tasks.

Why Transformers are Better Than LSTMs

1. No Sequential Processing

Transformers look at all the words in a sequence simultaneously, whereas LSTMs look at them one by one. This makes Transformers much faster, especially for long sequences.

2. Better Understanding of Long-Range Dependencies

In LSTMs, as the distance between words increases, the connection weakens. But Transformers can pay attention to any word in the sequence, no matter how far it is from the current word. This allows them to model long-range dependencies more effectively.

3. Efficient Parallelization

Transformers don’t depend on the previous word to process the next one, allowing them to process all words in parallel. This makes them highly efficient when scaling to large datasets.

4. Self-Attention

The self-attention mechanism allows the Transformer to focus on the most important words in the input sequence. This makes it much better at understanding context compared to LSTMs.

Difference between RNN, LSTM, GRU and Transformers

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In the Transformers library by Hugging Face, transformer models are categorized into three types based on their architecture: Encoder-only, Decoder-only, and Encoder-Decoder models. Here’s a breakdown of the models under each category:

1. Encoder-Only Transformers

These models are designed for tasks that involve understanding or classification (e.g., text classification, named entity recognition, sentence embedding). They only use the encoder part of the Transformer architecture.

Use Cases: Sentence classification, named entity recognition (NER), question answering (extractive), and sentence similarity.

RoBERTa

Facebook AI

Text classification, NER, QA

125M-355M params

Optimized BERT variant, trained on more data and longer sequences without the next sentence prediction.

DistilBERT

Hugging Face

Classification, NER, Sentiment analysis

66M params

A smaller, faster version of BERT with 97% of its performance.

ALBERT

Google

Sentence classification, NER, QA

12-18 layers, 12M-235M params

Parameter-efficient BERT variant with cross-layer parameter sharing.

ELECTRA

Google

Token classification, NER

14M-335M params

Trained with generator-discriminator approach for better sample efficiency than BERT.

DeBERTa

Microsoft

Text classification, NER, QA

48 layers, 1.5B params

Disentangled attention and enhanced mask decoder for improved performance.

ConvBERT

Google

Text classification, QA

110M params

Incorporates convolutional operations into the transformer for better efficiency and context.

XLNet

Google/CMU

Classification, QA, NER

24 layers, 340M params

Autoregressive model that captures bidirectional context, improving over BERT in many tasks.

Splinter

AI2

Question answering (QA), classification

BERT-like architecture

Fine-tuned BERT variant focused on question answering tasks.

LLAMA (Encoder)

Meta

Text understanding, classification

7B, 13B params

LLAMA in encoder mode used for text understanding tasks like classification and named entity recognition.

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2. Decoder-Only Transformers

These models are designed for generative tasks, where the model produces outputs token-by-token, often based on prior tokens. They use only the decoder part of the Transformer architecture.

Use Cases: Text generation, dialogue systems, code generation, story completion, and creative writing.

Model Name	Company	Mostly Used For	Specifications	Details

GPT-2

OpenAI

Text generation, dialogue systems

1.5B params

Popular model for text generation, used in creative writing and conversation generation.

GPT-3

OpenAI

Text generation, summarization, few-shot learning

175B params

Known for its strong few-shot learning and ability to generate human-like text; used via API.

GPT-4

OpenAI

Advanced text generation, reasoning tasks

1.8T params (speculated)

OpenAI’s most powerful model, used for complex text generation and reasoning, available via API.

GPT-Neo

EleutherAI

Text generation, dialogue systems

2.7B params

Open-source alternative to GPT-3, often used for similar generative tasks.

GPT-J

EleutherAI

Text generation

6B params

Another open-source GPT model for dialogue and text generation tasks.

LLAMA (Decoder)

Meta

Text generation, dialogue systems, QA

7B, 13B, 30B params

Meta’s LLAMA used for text generation and question answering.

OPT (Open Pretrained)

Meta

Text generation, summarization, dialogue systems

175B params

A large-scale GPT-like model open-sourced for research purposes.

DialoGPT

Microsoft

Conversational AI, dialogue generation

Based on GPT-2

Fine-tuned GPT-2 model for conversational tasks.

Cerebras-GPT

Cerebras Systems

Text generation, dialogue systems

111M-13B params

A series of GPT models optimized for Cerebras hardware.

Reformer

Google

Long text generation, dialogue systems

Varies

A more memory-efficient transformer capable of handling longer sequences.

Megatron-LM

NVIDIA

Text generation, summarization, dialogue systems

530B params

A high-performance GPT-like model optimized for NVIDIA hardware.

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3. Encoder-Decoder (Seq2Seq) Transformers

These models have both an encoder and a decoder, making them suitable for tasks that involve transforming one sequence into another, such as translation, summarization, or text generation with a specific output format.

Use Cases: Machine translation, text summarization, question answering (generative), text simplification, and paraphrasing.

Model Name	Company	Mostly Used For	Specifications	Details

T5

Google

Text-to-text tasks (translation, summarization, QA)

60M-11B params

Converts every task into a text-to-text format, suitable for translation and summarization.

BART

Facebook AI

Text generation, summarization, translation, QA

140M-400M params

Combines bidirectional encoder with autoregressive decoder, great for summarization and text generation.

Pegasus

Google

Abstractive summarization

568M params

Pre-trained with gap-sentence generation for improved abstractive summarization.

mBART

Facebook AI

Multilingual text generation, translation

610M params

A multilingual version of BART, capable of translation across multiple languages.

M2M-100

Facebook AI

Multilingual translation

418M-12B params

A many-to-many translation model that directly translates between 100 languages.

MarianMT

Hugging Face

Machine translation

Varies

Family of translation models supporting various language pairs, open-source.

ProphetNet

Microsoft

Text generation, summarization

139M params

Predicts future tokens for better text generation and summarization tasks.

XLM-RoBERTa

Facebook AI

Cross-lingual understanding, summarization, QA

550M params

Cross-lingual RoBERTa trained on 100 languages for multi-language tasks.

FLAN-T5

Google

Instruction-based text generation

80M-11B params

Fine-tuned for instruction-based tasks, improving zero-shot/few-shot performance across multiple tasks.

LLAMA (Encoder-Decoder)

Meta

Translation, summarization

7B, 13B, 30B params

LLAMA used in encoder-decoder mode for tasks like translation and summarization.

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Summary

• Encoder-Only Models: Focus on understanding tasks where the entire input sequence is visible to the model at once. Typically used for classification and extractive tasks.
• Decoder-Only Models: Focus on generation tasks where output is produced one token at a time, commonly used for text generation and autoregressive tasks.
• Encoder-Decoder Models: Used for sequence-to-sequence tasks, like translation, summarization, and generative question answering, where you need to transform input into output.

How to Choose

• Understanding Tasks (e.g., classification, NER): Use Encoder-Only models.
• Text Generation Tasks: Use Decoder-Only models.
• Sequence-to-Sequence Tasks (e.g., translation, summarization): Use Encoder-Decoder models.

Each category is tailored to different types of NLP tasks based on how they process input and generate output.

Conclusion

Transformers have revolutionized sequence modeling by addressing the core limitations of LSTMs. Their ability to process sequences in parallel, model long-range dependencies, and scale efficiently has made them the go-to architecture for a variety of NLP tasks. With the power of self-attention and efficient training, Transformers are setting new standards in fields like text generation, machine translation, and beyond.

As the field of AI continues to evolve, Transformers and their variants (like GPT, BERT, etc.) will remain at the forefront of advancements in sequence modeling.