Is Generative language model better than Representative one?

This post contains many subjective opinions based on my personal research and understandings, so please take it with a grain of salt.

Some reasons to start this post

Hey everyone! 👋

So, I was in an AI competition a few month ago, and something a mentor (more likely a speaker for a seminar) said really got me thinking. They mentioned a paper proving that Generative Language Models are better than Representative ones.

That got my gears turning! Is it really that simple? Also, I’m a BERT fan, so that’s really shocking me! Being the curious (and slightly contrarian :v) person I am, I decided to dig a little deeper.

Let’s get into it and explore this whole Generative vs. Representative Language Model thing.

What are We Talking About?

Before we go any further, let’s make sure we’re on the same page. What exactly are these “Representative” and “Generative” models?

Definitions

Think of it like this:

• Representative Models (aka Discriminative Models): Imagine a super-skilled art critic. They’re amazing at telling you what style a painting is, who might have painted it, and analyzing its meaning. They’re all about understanding and classifying existing stuff. In AI terms, they learn to map inputs to labels or categories. Think tasks like sentiment analysis, spam detection, or image classification. They’re representing the data in a way that helps you discriminate between different classes.

• Generative Models: Now picture a brilliant artist. They can actually create new paintings in different styles, maybe even in styles that haven’t existed before! They’re all about making new things that look like the stuff they’ve learned from. In AI, they learn the underlying patterns in data and can generate new, similar data. This is where we get cool stuff like text generation (ChatGPT), image synthesis, and even music composition. They’re generating new data instances.

Formally speaking:

• Representative Models: These models, also known as discriminative models, learn to estimate the conditional probability distribution \(P(y\|x)\), where \(y\) is the output label or category given the input data \(x\). Their primary goal is to discriminate between different classes or predict a specific output based on the input. They are trained on labeled data to find decision boundaries that effectively separate different classes.

• Generative Models: These models aim to learn the joint probability distribution \(P(x, y)\) or just the data distribution \(P(x)\). By learning this distribution, they can generate new samples \(x_{new}\) that are similar to the training data. In the context of language, this means generating text that resembles human writing in style and content. They are often used for tasks where creating new data instances is the objective.

Model Evolution Timeline

Let’s quickly trace how these model types developed over time (super simplified, of course!):

Representative Model Timeline (The “Understanding” Path):

• Early Days (Pre-2010s): Statistical models, RNNs starting to emerge for NLP. Focus on understanding language structure.
• 2010s - Rise of Deep Learning: CNNs and RNNs get deeper, better at feature extraction. Word embeddings (Word2Vec, GloVe) become key for representing words.
• 2017 - Transformers Change Everything
• 2018 - BERT Arrives! Bidirectional Transformers for deep contextual understanding. Encoder-focused architectures take center stage for NLU tasks.
• Post-BERT Era: Refinements and improvements to BERT (RoBERTa, ELECTRA, etc.). Focus on efficiency, better pre-training, and adapting to specific domains.

Generative Model Timeline (The “Creating” Path):

• Early Days (Pre-2010s): Statistical language models (n-grams), RNNs for basic text generation, but often limited and less coherent.
• 2014 - GANs Enter the Scene: Generative Adversarial Networks offer a new way to train generative models, especially for images.
• 2017 - Transformers Change Everything
• 2018 - GPT-1 is Born: OpenAI introduces the first Generative Pre-trained Transformer, showing the potential of large, transformer-based generative models.
• 2020 - GPT-3 Blows Minds: Massive scaling leads to emergent abilities and impressive text generation. Generative models become mainstream.
• Post-GPT-3 Era: Explosion of generative models! GPT-4, PaLM, LLaMA, Stable Diffusion, DALL-E. Focus on scale, multimodality, efficiency, and responsible AI.

BERT vs. GPT

Okay, so let’s talk about BERT and GPT. Both Representative and Generative models, in their modern form, are built upon the Transformer architecture.

Some PRs: Attention is all you need and much more

Following the introduction of the Transformer, model development began to diverge, leading to two primary paths:

• Representative Models (BERT Family): This path includes models like BERT, RoBERTa, and similar architectures. These models are predominantly encoder-only Transformers. Their architecture is optimized for tasks requiring deep contextual understanding of input text. They achieved state-of-the-art results on benchmarks like GLUE and SQuAD, which evaluate a model’s ability to understand context, identify relationships between words, and perform text classification. The strength of these models lies in their robust understanding of textual input.

• Generative Models (GPT Family): This path encompasses models such as GPT, PaLM, LLaMA, and related models. These architectures are often decoder-only or encoder-decoder Transformers, specifically designed for generative tasks. They gained prominence due to their remarkable ability to generate coherent and contextually relevant text. These models excel in text generation, chatbot development, and creative content applications, demonstrating the capacity of AI to produce novel textual outputs.

While both model families are rooted in the Transformer architecture, their design choices led to specialization in distinct capabilities: Representative models in understanding and Generative models in creation. This divergence marks a significant split in the landscape of modern Large Language Models.

Intuition for how they learn

Let’s get a little more intuitive. How do these models learn?

• Representative Models (Simplified Intuition): Think of them as learning to create really good “feature vectors” (embeddings) for your input data. Imagine you’re trying to classify pictures of cats and dogs. A representative model learns to transform each image into a vector that captures the key features that distinguish cats from dogs (ears, noses, fur patterns, etc.). Then, it uses a classifier (like a Feed-Forward Neural Network - FCNN) to take these feature vectors and project them into the right category (cat or dog). They learn to represent the data in a way that makes classification easy.

• Generative Models (Simplified Intuition): It’s a bit trickier to get intuition here, but think of them as learning the underlying rules of the data. Imagine you’re learning to write like Shakespeare. You don’t just memorize Shakespeare’s works; you try to understand the patterns in his language – his vocabulary, sentence structure, style, themes. A generative model tries to learn these patterns from its training data. Then, when you give it a prompt, it uses these learned patterns to generate new text that follows those rules and resembles the original data distribution. It’s like learning to mimic the “style” of the data.

Field Domination

The optimal choice between representative and generative models is highly dependent on the specific task and application. Neither model type is universally superior; rather, each excels in distinct domains.

Representative Models: Ideal for Understanding and Analysis

Representative models are particularly well-suited for tasks that require in-depth analysis and understanding of existing data:

• Natural Language Understanding (NLU) Tasks: These models are highly effective in core NLU tasks such as sentiment analysis, text classification, named entity recognition, and question answering (specifically extractive QA). Their architecture is designed for efficient and accurate extraction of semantic meaning from textual inputs.
• Structured Data Analysis and Prediction: Due to their strengths in classification and prediction, representative models are valuable tools for analyzing structured data. Applications include fraud detection, risk assessment, and providing decision support in areas like medical diagnostics, where accurate classification and prediction are paramount.
• Tasks Requiring Interpretability: Representative models often offer a higher degree of interpretability compared to generative models. This characteristic is crucial in sensitive domains like healthcare and finance, where understanding the reasoning behind a model’s output is essential for trust and accountability.

Generative Models: Leading in Creation and Novelty

Generative models, conversely, demonstrate dominance in tasks focused on content creation and open-ended applications:

• Natural Language Generation (NLG) Tasks: Generative models are the leading choice for NLG tasks, including text generation, creative writing, chatbot development, content creation across various media, code generation, and language translation. Their architecture is specifically designed to produce novel and contextually relevant content.
• Open-Ended and Creative Applications: When the objective is to leverage AI for creative endeavors, idea generation, or interactive experiences, generative models are the preferred solution. This includes applications in marketing content creation, entertainment media, and development of engaging AI interfaces.
• Knowledge-Intensive Tasks (in certain contexts): Emerging research indicates that generative models are increasingly capable in knowledge-intensive tasks. They show promise in applications requiring access to and synthesis of broad knowledge bases, such as answering complex queries that necessitate reasoning and information integration from diverse sources.

Summary of Task-Specific Strengths:

Feature	Representative Models	Generative Models
Primary Task Focus	Understanding & Classifying Input Data	Generating New Data Instances
Domain Strengths	NLU, Structured Data Analysis, Interpretability	NLG, Creative Applications, Knowledge-Intensive Tasks (Emerging)
Illustrative Examples	BERT, RoBERTa, Sentiment Classifiers	GPT, PaLM, Chatbots, Image Generators

The Comparison Table 📊

Okay, let’s get down to brass tacks. Here’s a comparison across different aspects:

Feature	Representative Models	Generative Models	Key Difference
Primary Task Focus	Classification, Prediction, Understanding	Generation, Creation, Synthesis	Understanding vs. Creating
Strengths	Precision, Accuracy, Interpretability, Efficiency	Creativity, Coherence, Novelty, Broad Application	Practicality & Control vs. Versatility & Imagination
Weaknesses	Can be less flexible for open-ended tasks, less “creative”	Can be computationally expensive, prone to bias/hallucinations, interpretability challenges	Limited Creativity vs. Resource Intensive & Less Controllable
Data Needs	Can work with smaller, task-specific labeled datasets	Thrive on massive, diverse datasets	Task-Specific vs. Data-Hungry
Computational Cost	Generally lower	Generally higher, especially for large models	Efficient vs. Resource-Intensive
Interpretability	Often better, especially simpler models	Can be “black boxes,” interpretability is a challenge	More Transparent vs. Less Transparent
Scaling Benefits	Accuracy & efficiency improve with scale	Emergent abilities, coherence improve significantly with scale	Incremental Improvement vs. Transformative Scaling
Example Tasks	Sentiment Analysis, Spam Detection, Image Classification	Text Generation, Chatbots, Code Generation, Image Synthesis	Classification/Prediction vs. Generation/Creation
“Better For…“	Tasks requiring precision, reliability, interpretability	Tasks requiring creativity, novelty, open-endedness	Specific Use Cases Dictate “Better”

Which One is “Better”?

Alright, the million-dollar question! Is Generative totally better than Representative?

The answer, as with most things in AI, is… IT DEPENDS! 😅

There’s no single “better” model type across the board. It really boils down to:

• What’s your goal? What do you want the model to do?
• What kind of data do you have?
• What are your resource constraints? (Compute, time, budget, etc.)

If you need AI to be:

• Precise, Reliable, and Explainable: Representative models are your champions! Think critical applications where accuracy and trust are paramount.
• Creative, Conversational, and Generate Novel Content: Generative models are the way to go! They unlock possibilities for new forms of AI interaction and creative expression.

It’s not about “better,” it’s about “better suited.”

Scientific proof (Papers)

Don’t just take my word for it! Research backs this up:

• For tasks like Aspect-Based Sentiment Analysis, studies show Discriminative (Representative) models still outperform Generative models in cross-lingual and cross-domain settings (Discriminative Models Still Outperform Generative Models).
• In Question Answering, it’s context-dependent. Extractive (Representative-style) readers are better for short-context QA and out-of-domain generalization, while Generative readers excel in long-context QA (Machine Reading Comprehension).
• For Text Generation, while Generative models are the focus, Masked Language Models (a type of Representative model in some ways) can actually outperform Causal Language Models (Generative) in certain text generation tasks (Exploration of Masked and Causal Language Modelling for Text Generation).

Personal Thoughts

Personally, I gotta admit, I’ve always had a soft spot for Representative models.

Maybe it’s my inner data scientist coming out. :v

But, you can’t deny the sheer wow factor of Generative models! They’re bringing AI to applications we could only dream of a few years ago. Chatbots that (almost) feel human, AI writing assistance, creative tools… it’s mind-blowing! And for end-user applications, especially for things like chatbots and content creation, Generative models are definitely where the excitement is right now.

The “Generative vs. Representative” debate isn’t really about picking a winner. It’s about understanding the strengths and weaknesses of each approach and choosing the right tool (or combination of tools!) for the task at hand.

What are your thoughts? Are you Team Generative or Team Representative? Or maybe Team “Both are Awesome!”? Let me know in the comments below! 👇