Transformer Models_ BERT vs. GPT.pdf

28/09/2023, 18:30 Transformer Models: BERT vs. GPT
https://chat.openai.com/c/53569360-910f-431e-abd9-1542776c1e6f 1/14
You:
I am trying to undertand how Transformer work . Few types are : 1. Encoder only : Example BERT ,
how it works is it performs 2 task . Masked Language Modeling and Next Sentence Prediction . 2.
Decoder only : Like GPT , it tries to just predict 1 word after other . Is my understanding correct ?
------------------
ChatGPT:
Yes, your high-level understanding is largely correct, but let me provide some more details to help
you understand the functioning of Transformers, particularly BERT (Encoder-only) and GPT
(Decoder-only) architectures:
BERT (Encoder-only):
BERT, which stands for Bidirectional Encoder Representations from Transformers, uses only the
encoder part of the Transformer architecture. It is trained using two main tasks:
1. Masked Language Modeling (MLM):
Randomly masks some of the tokens from the input.
The model then tries to predict the original vocabulary id of the masked word based on
its context.
Unlike traditional left-to-right language model training, this allows the model to consider
the context from both directions.
2. Next Sentence Prediction (NSP):
Given two sentences, A and B, the model learns to predict if B follows A in the original
text.
This helps the model understand relationships between sentences.
GPT (Decoder-only):
GPT, or Generative Pretrained Transformer, uses only the decoder part of the Transformer
architecture. It is trained using a different approach:
1. Autoregressive Language Modeling:
The model is trained to predict the next word in a sequence, given all the previous
words.
It learns to generate coherent and contextually relevant text one word at a time.
Since GPT is autoregressive and only uses the decoder, it only attends to the left
context (previous words) when making predictions.
Summary:
BERT:
Uses only the encoder.
Trained with MLM and NSP.
Considers bidirectional context.
GPT:
Uses only the decoder.
Trained with autoregressive language modeling.

Considers only the left context when predicting the next word.
These models, once pre-trained, can be fine-tuned on a variety of downstream tasks like text
classification, named entity recognition, question answering, and more. The versatility and
capability of Transformer models like BERT and GPT have led to their widespread adoption in
natural language processing (NLP) applications.
------------------
You:
So in Autoregressive Language Modeling , lets The Sky is ____ , now to fill in the blank will it use
all the previous keyowrd which is The , Sky , is OR will it only use 'is' ?
------------------
ChatGPT:
In autoregressive language modeling, as exemplified by GPT, the model uses all available previous
words as context to predict the next word. So, in the example "The Sky is ____", the model
considers the entire given context — "The", "Sky", and "is" — to predict the blank.
Here's a more detailed step-by-step explanation:
1. Tokenization: The input sentence is tokenized into individual words or subwords. In this
case, the tokens are ["The", "Sky", "is"].
2. Encoding: Each token is then converted into an embedding, which is a high-dimensional
vector representation. The embedding layer is typically followed by positional encoding to
maintain the order of the words.
3. Self-Attention: The Transformer model processes the input sequence through layers of self-
attention mechanisms. In each layer, every token attends to every other token in the
sequence, including itself. This means that the representation of each word is influenced by
all other words in the context, weighted by the attention scores.
4. Prediction: The final representation of the last token ("is") is used to predict the next word in
the sequence. The model outputs a probability distribution over the vocabulary, and the word
with the highest probability is selected as the prediction.
So, to summarize, the model does not only use the immediate preceding word "is" but utilizes the
entire given context "The Sky is" to make the prediction for the blank.
------------------
You:
For BERT wouldn't only using Masked Language Modeling sufficient ?
------------------
ChatGPT:

Yes, the Masked Language Modeling (MLM) task is indeed a powerful training objective, and it has
been shown to be effective for learning contextualized word representations. MLM enables the
model to learn bidirectional context, which is key to understanding the meaning of a word in a
sentence.
However, the Next Sentence Prediction (NSP) task was introduced in BERT to help the model learn
relationships between sentences, which is something that MLM alone doesn’t explicitly teach. NSP
is designed to help the model understand the coherence and relationship between pairs of
sentences, which is important for many downstream tasks like question answering, text
summarization, and natural language inference.
That said, subsequent research has shown that NSP is not always necessary for achieving strong
performance on downstream tasks. For instance, models like RoBERTa, a variant of BERT, omit
the NSP objective and instead train with only MLM on larger datasets and with longer sequences,
and they have been shown to achieve even better performance than BERT on several benchmarks.
So, while MLM alone is indeed a strong training signal, the addition of NSP was initially thought to
be beneficial for learning inter-sentence relationships. However, it's not strictly necessary, and there
are alternative approaches for training powerful language models.
------------------
You:
Architecturally if u see "Encoder-only" and "Decoder-Only" both have an Attential Layer and Feed-
Forward layer . Where is the difference ? I am wondering can we do Masked Language Modeling in
a Decoder only architecture and vice versa .
------------------
ChatGPT:
The architectural distinction between "Encoder-only" and "Decoder-only" models like BERT and
GPT primarily resides in the nature of their attention mechanisms, which affects how each model
type can be utilized:
1. Encoder-only (BERT):
The attention mechanism in the encoder is self-attention. Each token in the input
sequence attends to every other token, including itself.
This allows the model to capture bidirectional context, making it suitable for tasks like
Masked Language Modeling (MLM), where the model predicts a masked token based
on its surrounding context.
2. Decoder-only (GPT):
The attention mechanism in the decoder is also self-attention, but it is causal or
autoregressive. Each token can only attend to previous tokens in the sequence and not
the future tokens.
This architecture is designed for generating sequences one token at a time and is used
for autoregressive language modeling, where the model predicts the next token based
on the preceding tokens.

Can we do Masked Language Modeling in a Decoder-only architecture?
Theoretically, yes, but it would require modifying the attention mechanism to allow each token
to attend to all other tokens in the sequence, not just the previous ones. This would make the
decoder behave more like an encoder in terms of attention. However, this would also mean
losing the autoregressive property, which is essential for sequence generation tasks.
Can we do autoregressive language modeling in an Encoder-only
architecture?
Not directly, since the encoder processes the entire sequence at once and doesn’t have the
autoregressive property of generating one token at a time based on previous tokens.
However, there are techniques like “prefix tuning” where you can adapt encoder-only models
for autoregressive generation tasks.
Summary:
While both encoder and decoder have attention layers and feed-forward layers, the key difference
is in the attention mechanism. Encoder attention is bidirectional, while decoder attention is
causal/autoregressive. Adapting each architecture for tasks they are not natively suited for is
possible but involves non-trivial modifications or techniques.
------------------
You:
What is the use of BERT ?
------------------
ChatGPT:
BERT (Bidirectional Encoder Representations from Transformers) is a powerful and versatile pre-
trained language model that has been a catalyst for the advancement of Natural Language
Processing (NLP). The primary uses of BERT are in fine-tuning for a wide array of downstream
NLP tasks and as a feature extractor for text representations. Here are some of the primary
applications of BERT:
1. Text Classification:
BERT can be fine-tuned to classify text into various categories, making it useful for
sentiment analysis, topic categorization, and other classification tasks.
2. Named Entity Recognition (NER):
Fine-tuning BERT can help identify and classify entities in the text, such as people,
organizations, locations, date expressions, and other domain-specific terms.
3. Question Answering:
BERT is widely used for developing question-answering systems, where it can
understand the context of a passage and answer questions about its content.
4. Semantic Textual Similarity:

BERT can be used to measure the similarity between pairs of sentences, which is
valuable for applications like duplicate detection, paraphrase identification, and
information retrieval.
5. Coreference Resolution:
By leveraging BERT's contextualized representations, systems can be developed to
determine when different expressions in text refer to the same entity.
6. Text Generation:
Though not as natural for text generation as decoder-based models like GPT, BERT can
still be used (with modifications) for tasks like text summarization and paraphrasing.
7. Feature Extraction:
BERT embeddings can be used as feature vectors for text in a variety of machine
learning models, offering rich, context-aware representations of the input.
8. Language Understanding:
BERT's pre-training tasks, MLM and NSP, help it understand grammar, context, and
relationships between sentences, making it useful for numerous tasks requiring
language understanding.
9. Zero-shot Learning:
BERT can be used for zero-shot learning, where it can make predictions on tasks for
which it has not seen any labeled examples during training.
10. Cross-lingual Transfer:
Multilingual versions of BERT can be used for cross-lingual transfer learning, enabling
the development of NLP applications for languages with limited labeled data.
BERT's versatility and capability to understand the nuances of natural language have made it one
of the foundational models for developing a myriad of NLP applications across different domains
and industries.
------------------
You:
Ok with an example help me understand how come BERT is able to do Text Classification .
------------------
ChatGPT:
Certainly! Let's walk through how BERT can be used for text classification using a concrete
example. Suppose we want to classify movie reviews into two categories: "positive" and "negative".
1. Preprocessing:
First, we start by preprocessing our movie review text. This involves tokenizing the text and
converting it to input features that BERT understands. For BERT, this involves:

Tokenizing the text into subwords or word pieces.
Adding special tokens [CLS] at the beginning and [SEP] at the end of the sequence.
Padding or truncating the sequence to a fixed length.
Creating attention masks to differentiate between actual tokens and padding.
For example, if we have a review "Great movie!", it might be tokenized and processed as:
css
[CLS] great movie ! [SEP]
2. Feeding Input to BERT:
Once the input is prepared, we feed it into the BERT model. BERT processes the entire sequence
of tokens and produces a corresponding sequence of contextualized embeddings.
The embedding corresponding to the first token [CLS] (known as the "CLS embedding") is of
particular interest, as it is designed to hold the aggregate representation of the entire sequence and
is used for classification tasks.
3. Fine-Tuning and Classification Layer:
For text classification, we add a classification layer on top of the BERT model. This layer takes the
CLS embedding as input and outputs the probabilities for each class (in this case, "positive" and
"negative").
During fine-tuning, we train the model on a labeled dataset of movie reviews. The model learns to
adjust its parameters, including those in the BERT encoder and the classification layer, to predict
the correct label for each review.
For example, if the input review is "Great movie!", the model should learn to give higher probability
to the "positive" class.
4. Model Training:
During training, the model is exposed to many examples of movie reviews and their corresponding
labels. The model learns by adjusting its weights to minimize the difference between its predictions
and the actual labels (this difference is quantified by a loss function).
For instance, when the model sees many examples of reviews with words like "great", "amazing",
"excellent" labeled as "positive", it learns the association between these words and the positive
sentiment.
5. Prediction:
Once the model is fine-tuned, we can use it for classifying new, unseen movie reviews. When given
a new review, the model processes the input, produces the CLS embedding, and the classification
layer outputs the probabilities for each class. The class with the highest probability is predicted as
the label for the input review.
Summary:
BERT, by leveraging its pre-trained contextual embeddings and a fine-tuned classification layer,
can effectively classify text into different categories. The model learns the nuances and features
associated with each class during training, enabling it to make accurate predictions on unseen
data.

------------------
You:
Can't we apply the same logic to GPT to add a classification layer, and adjust its weights to
minimize the difference between its predictions and the actual labels
------------------
ChatGPT:
Absolutely, you can indeed apply a similar approach to fine-tune GPT for classification tasks.
Here’s a step-by-step explanation of how you can adapt GPT for text classification, such as
sentiment analysis:
1. Preprocessing:
Like with BERT, you tokenize the input text, add special tokens, and create the necessary
input features. For GPT, you typically add a start-of-sequence token at the beginning.
2. Feeding Input to GPT:
You feed the processed input sequence into the pre-trained GPT model. GPT processes the
sequence autoregressively and produces a sequence of embeddings.
3. Classification Layer:
For classification, you can add a linear layer on top of the final token’s embedding (instead of
the start-of-sequence token as in BERT).
This linear layer will output the logits or probabilities for each class.
4. Fine-Tuning:
You fine-tune the model on a labeled dataset for the classification task.
The model's parameters, including the GPT layers and the classification layer, are updated to
minimize the loss between the model's predictions and the actual labels.
5. Prediction:
After fine-tuning, the model can be used to classify new, unseen texts.
The model processes the input text, and the classification layer outputs the probabilities for
each class.
The class with the highest probability is assigned as the label for the input text.
Summary:
While GPT models are primarily designed for generative tasks, they can certainly be adapted and
fine-tuned for discriminative tasks like text classification by adding a classification layer and training
on labeled data. In practice, both BERT and GPT have been successfully fine-tuned for a wide
range of NLP tasks, demonstrating their versatility.

------------------
You:
If that's the case can't all the tasks possible by BERT be performed by GPT ?
------------------
ChatGPT:
Yes, in theory, GPT and other autoregressive, decoder-only models can be adapted and fine-tuned
to perform essentially all the tasks that BERT and other encoder-only models can, and vice versa.
Both model types have demonstrated versatility and effectiveness across a wide range of NLP
tasks, including text classification, named entity recognition, question answering, and more.
However, there are some practical considerations and differences between the two that might make
one more suitable or efficient for a particular task than the other:
1. Bidirectional Context:
BERT inherently captures bidirectional context, which can be advantageous for tasks
that require understanding the entire input sequence, such as masked language
modeling and certain types of question answering.
2. Autoregressive Nature:
GPT is designed to generate sequences autoregressively, making it particularly suited
for text generation tasks like translation, summarization, and creative writing.
3. Training Objective:
The pre-training objectives of BERT (Masked Language Modeling) and GPT (Causal
Language Modeling) are different, leading to differences in what kind of information and
patterns they prioritize and capture during pre-training.
4. Fine-Tuning Efficiency:
Depending on the task and the available data, one model might require less data or
compute resources to fine-tune effectively compared to the other.
5. Task Adaptation:
Adapting the models for certain tasks might require specific architectural modifications
or training techniques, which can be more straightforward for one model type than the
other depending on the task.
6. Recent Advancements:
Innovations and advancements in model architectures and training techniques might
lead to one model type outperforming the other on specific tasks or benchmarks.
While both BERT and GPT can be adapted for a wide array of tasks, the choice between them
often depends on the specific requirements of the task, available resources, and empirical
performance on relevant benchmarks. Additionally, the introduction of models like T5 and BART,

which combine both encoder and decoder components, showcase that a hybrid approach can also
be highly effective across tasks.
------------------
You:
My last question , Let's say i have a News database . Now i create 2 Text embedding --> one using
GPT and other using BERT . Now i take a new sentence and calculate cosine similarity in both the
embedding , we should get almost similar result r8 ?m
------------------
ChatGPT:
In theory, if both GPT and BERT are well-tuned and the embeddings are generated effectively, they
should capture the semantic meaning of the text, and you might expect similar cosine similarity
results when comparing a new sentence to the existing database. However, there are several
factors that can contribute to differences in the results:
1. Model Architecture and Training:
BERT and GPT have different architectures and are pre-trained with different objectives.
BERT uses a bidirectional approach, capturing context from both directions, while GPT
is autoregressive, capturing context only from previous tokens. These differences can
lead to variations in the embeddings they produce.
2. Tokenization and Input Representation:
The way the models tokenize text and represent input sequences can differ, potentially
affecting the resulting embeddings.
3. Dimensionality and Pooling Strategy:
The dimensionality of the embeddings and the strategy used to pool token embeddings
to form sentence embeddings (if applicable) can influence the similarity scores.
4. Fine-tuning and Task Adaptation:
If the models are fine-tuned on different tasks or datasets, the specialization they
acquire can affect the semantic space they encode, leading to differences in similarity
results.
5. Model Variants and Versions:
Different versions or variants of BERT and GPT may have been trained on different
datasets, with different hyperparameters, or for different numbers of training steps,
which can affect their performance and the embeddings they produce.
In practice, while both BERT and GPT are capable of generating meaningful and semantically rich
embeddings, there is no guarantee that the cosine similarity scores between embeddings from the
two models will be identical or even highly correlated for all input sentences. It is advisable to
empirically evaluate and compare the performance of the embeddings from both models on the
specific task or similarity measure you are interested in to determine their suitability and reliability.

------------------
You:
Now for User News recommedation which embedding will give better results ?
------------------
ChatGPT:
For user news recommendation, the choice of embedding—whether from BERT, GPT, or another
model—depends on the specific requirements of the task, the nature of the data, and the desired
user experience. Both BERT and GPT can potentially yield effective results, but they have
differences that might make one more suitable than the other in certain scenarios. Here are some
considerations to help you decide:
1. Contextual Understanding:
BERT’s bidirectional context might offer a more comprehensive understanding of the
content, potentially capturing nuances that are beneficial for recommending news
articles based on user preferences.
2. Generative Capability:
GPT’s autoregressive nature might be advantageous if the recommendation system
involves generating descriptive or explanatory text, such as personalized summaries or
reasons for recommendations.
3. Computational Efficiency:
Depending on the implementation, one model might be more computationally efficient
than the other for generating embeddings, which could be a consideration if real-time
recommendations are required.
4. Fine-Tuning and Adaptation:
Consider which model can be more effectively fine-tuned or adapted to the specific
nature of news content and user interaction data available.
5. Empirical Performance:
Experiment with both models and evaluate their performance empirically on a validation
set or through A/B testing to determine which one better meets the objectives of the
recommendation system.
6. Hybrid Approaches:
Explore hybrid approaches that combine the strengths of both models or incorporate
additional features, such as user behavior, click patterns, and reading time, to enhance
recommendation quality.
7. User Feedback and Interaction:
Consider incorporating mechanisms for user feedback and interaction to continuously
refine and adapt the recommendation model to evolving user preferences.

Ultimately, the effectiveness of the embedding for user news recommendation is not solely
determined by the choice of BERT or GPT but also by how well the model is adapted to the task,
the quality of the data, and the overall design of the recommendation system. It's often worthwhile
to experiment with different approaches and models to identify the solution that best meets the
goals of the recommendation system and delivers the most value to the users.
------------------
You:
can you explain your first point "BERT’s bidirectional context might offer a more comprehensive
understanding of the content" with some example where BERT is advantageous .
------------------
ChatGPT:
Certainly! BERT’s bidirectional context means that it considers both the left and right context of
each token in a sequence when producing embeddings. This can lead to a richer and more
comprehensive understanding of the content, making BERT particularly advantageous for tasks
where understanding the relationships between different parts of a text is crucial.
Example:
Let’s consider a news article snippet:
"Apple has plans to launch a new product. However, the fruit market is not affected."
In this example, the word "Apple" has two potential meanings: it could refer to the technology
company or to the fruit. The word "product" following "Apple" might suggest that it refers to the tech
company. However, the second sentence talks about the "fruit market," which could create
ambiguity if the model is not able to understand the context comprehensively.
BERT's Bidirectional Context:
BERT reads the entire sequence at once, considering both the preceding and following
tokens for each word. When it encounters the word "Apple," it considers both the mention of
launching a "new product" and the context of the "fruit market" in the second sentence.
This bidirectional understanding helps BERT disambiguate that the first mention of "Apple"
refers to the technology company, not the fruit, even though there is mention of a "fruit
market" later in the sequence.
Unidirectional Context (e.g., GPT):
GPT, on the other hand, processes the sequence autoregressively, from left to right. When it
sees the word "Apple," it has information about the "new product" but doesn’t yet know about
the mention of the "fruit market" later in the sequence.
While GPT is still very powerful and can often understand context well, it might, in some
cases, be at a disadvantage compared to BERT for tasks that benefit from a complete,
bidirectional view of the context.
Advantage in Tasks:

This bidirectional context is particularly advantageous in tasks like Named Entity Recognition,
Coreference Resolution, and Relationship Extraction, where understanding the relationships and
references between different parts of a text is important.
For instance, in the above example, if the task is to identify company names in the text, BERT’s
bidirectional context can help it accurately recognize "Apple" as a company, despite the potentially
confusing mention of the "fruit market" later in the text.
------------------
You:
Ok now let's take this "Apple has plans to launch a new product. However, the fruit market is not
affected" . If we create a text emedding of this in BERT and calucalte cosine similarty with say
"Tesla" & "Orange". Tesla will have less distance and Orange should have larger distance . But in
GPT text embedding . both "Tesla" and "Orange" would have shorter distance ?
------------------
ChatGPT:
While it's not definitively predictable without conducting an actual experiment, your hypothesis
could indeed be plausible given the inherent differences between BERT and GPT.
BERT:
Given BERT’s bidirectional understanding of context, it might be more likely to recognize that
"Apple" in the given sentence refers to the technology company rather than the fruit. Therefore,
when calculating cosine similarity:
The embedding of "Apple" in the sentence context may be closer to the embedding of "Tesla"
(another technology company) than to "Orange" (a fruit).
GPT:
GPT generates representations based on the left-to-right context. While it's still a powerful model
capable of understanding context and disambiguation, there might be scenarios where it’s less
effective at disambiguating meanings compared to a bidirectional model, especially when the
disambiguating context appears later in the sentence.
In such cases, GPT might not as effectively differentiate between the company and fruit
meanings of "Apple," potentially resulting in embeddings where both "Tesla" and "Orange"
have shorter distances to the sentence embedding.
Additional Considerations:
Model Training and Knowledge: Both BERT and GPT have been trained on large corpora
and have knowledge of real-world entities and their attributes. They might still differentiate
between "Apple" the company and "Apple" the fruit based on this prior knowledge, even if the
immediate sentence context is ambiguous.
Experimentation: It is always recommended to validate such hypotheses through
experimentation, as model behavior can be influenced by various factors including

tokenization, input representation, model parameters, and the specific variant of the model
used.
Fine-tuning: If a model is fine-tuned on a relevant task or domain-specific corpus, it might
exhibit enhanced performance and better disambiguation capabilities.
In summary, while your hypothesis is reasonable given the differences between BERT and GPT,
the actual outcome might vary, and experimentation is the best way to observe the behavior of the
models in such scenarios.
------------------
You:
I am asking this example to just understand how could 2 embedding differe , it need work out in
reality for this particular example .
------------------
ChatGPT:
Absolutely, your example is a good conceptual exploration to understand how embeddings might
differ between BERT and GPT due to their architectural differences. Here's a more detailed
breakdown to further clarify this:
BERT:
BERT’s ability to consider bidirectional context means it processes both the preceding and the
following context around each token. In the case of the sentence "Apple has plans to launch a new
product. However, the fruit market is not affected," BERT can utilize the entire context to
disambiguate that "Apple" here refers to the tech company, not the fruit. When comparing
embeddings:
Embedding with "Tesla": The context around "Apple" is about launching a new product,
which is a context more related to companies like "Tesla" than to fruits. The cosine similarity
between this sentence embedding and the embedding for "Tesla" is likely to be higher.
Embedding with "Orange": Given the context of launching a product, the embedding of the
sentence is likely to be less similar to the embedding of a fruit like "Orange."
GPT:
GPT processes text in a left-to-right manner. When it processes the word "Apple" in the given
sentence, it doesn’t have the information about the "fruit market" mentioned later. While GPT is
sophisticated and often effectively understands context, its unidirectional nature might, in some
cases, make it less effective in disambiguating compared to BERT.
Potential Outcome: GPT might generate embeddings where the semantic distance between
the sentence and "Tesla" and between the sentence and "Orange" could be relatively closer
compared to BERT, especially if the distinguishing context appears later in the sentence.
Conceptual Takeaway:

The key conceptual takeaway here is that the bidirectional context in BERT could lead to more
accurate disambiguation and, consequently, to embeddings that better reflect the semantic
relationships in the text. GPT, while still powerful, relies on a unidirectional context, which might
impact its ability to generate embeddings that accurately capture relationships, especially when the
context for disambiguation appears later in the sentence.
It’s important to note that while this conceptual exploration helps in understanding potential
differences, the actual behavior of the models in practice can be influenced by various factors and
would need to be validated through experimentation.
------------------

Transformer Models_ BERT vs. GPT.pdf

Transformer Models_ BERT vs. GPT.pdf

Transformer Models_ BERT vs. GPT.pdf

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