Exploring GPT Models: Stanford Seminar 2022

Exploring GPT Models: Stanford Seminar 2022

Table of Contents

1. Introduction
2. The Evolution of Language Modeling
   • From the Deep Learning Boom to GPT-2 and GPT-3
   • The Power of Autoregressive Generative Models
3. Language Modeling in Different Modalities
   • Applying GPT to Images
   • DALL-E: Jointly Modeling Text and Images
4. Introducing Codex: Code Writing Models
   • Why Train Models on Code?
   • Evaluating Codex using Unit Tests
   • The Unreasonable Effectiveness of Sampling
   • Fine-tuning Codex on Specific Data Sets
5. Conclusion
   • Recap of Main Points
   • Acknowledgments

Introduction

In recent years, language modeling has seen rapid progress thanks to deep learning techniques. One notable example is the Generative Pre-trained Transformer (GPT) series, including GPT-2 and GPT-3. These autoregressive generative models have shown impressive capabilities in understanding and generating text. However, the potential of language modeling extends beyond just text. It can be applied to various modalities, including images and code.

This article explores the evolution of language modeling, from the deep learning boom to the advancements in GPT-2 and GPT-3. It also delves into the application of language models in different modalities, such as generating images using GPT and joint modeling of text and images with DALL-E. Lastly, it introduces Codex, a code writing model, discussing its purpose and evaluation methodologies.

The Evolution of Language Modeling

From the Deep Learning Boom to GPT-2 and GPT-3

The deep learning boom, which started in 2012 with AlexNet, revolutionized the field of computer vision. This breakthrough showcased the power of deep neural networks in learning and generalizing from vast amounts of labeled image data. However, applying similar techniques to language modeling was challenging due to the scarcity of labeled text data.

GPT-2 and GPT-3 emerged as milestones in the evolution of language modeling. GPT-2, trained on a large corpus of internet text data, demonstrated impressive capabilities in generating coherent and contextually Relevant text. It showcased the potential of autoregressive generative models in capturing the Patterns and relationships within language.

GPT-3 further pushed the boundaries of language modeling. With its 175 billion parameters, it achieved even higher levels of coherence and understanding. The model excelled in zero-shot translation, reading comprehension, and summarization tasks, demonstrating its adaptability to different language-related problems.

The Power of Autoregressive Generative Models

Autoregressive generative models, like GPT-2 and GPT-3, have proven to be universal in their capabilities. They can not only generate text but also be applied to other modalities, such as images. The key Insight is that any data, including images, can be represented as sequences of bytes. Thus, the techniques used in language modeling can be adapted to model and generate other modalities.

By applying GPT to image generation, researchers found that the models could generate coherent images by predicting the next pixel in the image sequence. Although modifications may be needed to incorporate 2D biases and handle varying image sizes, the underlying autoregressive framework remains effective. These models Show promise in image generation, colorization, and even semantic transformations.

Similarly, DALL-E demonstrated the power of joint modeling of text and images using GPT. The model can generate images Based on text Prompts, showcasing an understanding of the desired concepts and the ability to render them visually. It opens up possibilities for creating content based on textual descriptions, such as producing illustrations or designs.

Language Modeling in Different Modalities

Applying GPT to Images

While GPT was primarily designed for text-based language modeling, it can be adapted to generate images. By training GPT on images represented as sequences of pixels, the model can predict the next pixel in the sequence to generate coherent images. This adaptability allows the model to generate diverse and contextually appropriate visual content.

The generated images exhibit characteristics similar to the training data, showing the potential for GPT to understand and replicate visual patterns. The model can generate images of objects, scenes, or even follow given prompts to Compose specific visuals. The results, while not Flawless, demonstrate GPT's ability to generate Meaningful images based on textual descriptions.

DALL-E: Jointly Modeling Text and Images

DALL-E takes the concept of modeling text and images further by combining both modalities in a single model. By training on paired text-image datasets, DALL-E can generate images conditioned on specific text prompts and vice versa. This joint modeling approach allows the model to capture the relationships between text and images, enabling it to Create visually relevant and conceptually coherent content.

DALL-E's performance is impressive in tasks such as image captioning, image generation from textual descriptions, and even semantic transformations on images. It shows that language models can be extended to handle multiple modalities and perform complex tasks that require both textual and visual understanding.

Introducing Codex: Code Writing Models

One specific application of language modeling is generating code. Codex is OpenAI's code writing model, trained on a vast amount of code from GitHub repositories. By fine-tuning GPT-3 models on code data, Codex demonstrates the ability to generate functional code based on given prompts and requirements.

Evaluating code generation models brings its own challenges. While metrics like BLEU are commonly used in language evaluation, they are insufficient for assessing functional correctness in code generation. Instead, unit tests provide a more reliable measure of code correctness. OpenAI created a dataset with handwritten programming problems, including the desired function behavior and accompanying unit tests, to evaluate Codex and other code generation models.

The pass @ k metric is introduced to assess the success rate of generated code samples. By generating a large number of samples and counting the correct ones, the metric provides a more accurate measure of functional correctness. It reveals that Codex's performance significantly improves with a larger number of samples, making sampling an effective technique for code generation.

Conclusion

In conclusion, language modeling has undergone remarkable progress, driven by models like GPT-2 and GPT-3. These autoregressive generative models have proven their versatility in capturing patterns and relationships within language. Moreover, their capabilities extend beyond text to other modalities, including images and code.

The application of language models to images has shown promising results in generating coherent and contextually relevant visuals. Models like DALL-E have demonstrated the potential for jointly modeling text and images, allowing for generating meaningful content informed by textual prompts.

Specifically, in the realm of code generation, Codex showcases the power of fine-tuning models on code data. Evaluating these models based on unit tests proves critical in assessing functional correctness. Sampling techniques, coupled with appropriate ranking heuristics, further enhance the performance of code generation models.

In summary, advancements in language modeling, coupled with their applicability to various modalities, open up exciting possibilities for developing AI models that can understand and generate content in different domains. Acknowledgments go to the researchers, mentors, and teams at OpenAI who have contributed to these advancements. The future of language modeling holds even more potential for bridging the gap between humans and machines in understanding and generating content.