Faking the News with Natural Language Processing and GPT-2


Natural Language Processing is Fun Part 4

GPT-2 has transformed text generation, producing output that is more realistic than ever before. OpenAI was so impressed by the quality of its text that they decided not to release the full GPT-2 model, fearing it could be misused to create misleading fake news that might deceive the public or overwhelm search engines like Google.

This raises a daunting question: how easy is it for the average person to generate fake news that could convincingly trick real readers? Let’s explore the capabilities of systems like this and consider the threats they pose.

Creating a Fake News Website

To visualize our findings, we’ll build a fictitious newspaper called “News You Can’t Use,” showcasing AI-generated content.

An illustrative image showcasing a fake news website called 'News You Can't Use'. The design should mimic a newspaper layout with a title at the top, featuring clearly fake headlines and articles generated by AI. Include elements like a 'breaking news' banner, and a variety of article snippets showcasing absurd yet plausible fake stories. The overall tone should be humorous and satirical to emphasize that it is AI-generated content.

We’ll create a Python script to mimic a news site like the New York Times, generating artificial news stories while assessing the quality of the output and discussing the ethical dilemmas this technology introduces.

Understanding GPT-2’s Capabilities

GPT-2 is immensely knowledgeable due to being trained on vast amounts of text from the internet. It generates realistic sentences based on this knowledge. For example, starting with the text “Abraham Lincoln” might yield a response containing plausible yet false historical details.

This ability stems from two main factors: the scale of the model (1.5 billion parameters) and the innovative Transformer architecture it utilizes.

How GPT-2 Works

The architecture relies on context and statistical modeling rather than a straightforward database of facts. Older models, like Bag of Words, fail to grasp the sequence and context of words, which is crucial for human language.

In contrast, RNNs (Recurrent Neural Networks) improved this by processing words in sequence. However, they struggled with longer texts until the Attention mechanism was introduced, allowing models to focus on specific words.

Transformers, the foundation of GPT-2, utilize this concept further, maintaining contextual awareness across entire paragraphs. They model text significantly more efficiently than RNNs, enabling better coherence and relevance in generated text.

A detailed illustration of the Transformer model architecture used in GPT-2. Show the input layers, attention mechanisms, and output layers. Include labels for essential components such as 'Input Embeddings', 'Self-Attention', and 'Output Predictions'. Use a clean and educational design to effectively communicate how the Transformer model processes text. Add arrows to indicate the flow of data through the model.

Grover: Better Control Over News Generation

To generate fake news articles responsibly, we can use Grover, which builds on GPT-2. Grover understands contextual elements like headlines and author names, enabling it to produce coherent and related outputs.

It models news stories as having distinct components:

  • Domain name (e.g., nytimes.com)
  • Author name
  • Date published
  • Headline text
  • Body text

This knowledge allows Grover to generate realistic articles based on specific headlines.

An image depicting the Grover model architecture for generating fake news. Include elements such as input data like 'headline', 'domain name', 'author', and 'body text', demonstrating how Grover generates realistic news based on these inputs. Use different colors for each component and arrows to indicate how they interconnect. Aim for a clear, informative layout that explains the process visually.

The Implications and Ethical Considerations

While GPT-2 and similar models represent exciting advancements in machine learning, they carry risks of misuse. There’s a growing conversation around how best to manage and regulate their capabilities. Despite OpenAI’s initial decision to restrict access, researchers have since reproduced the model with alarming ease.

Ultimately, these technologies can either propagate misinformation or aid in the fight against it. Grover, originally designed for news generation, proves to be equally effective in fake news detection—demonstrating the potential for dual-purpose applications in combatting misinformation.

As the generation of fake news becomes increasingly sophisticated, it’s critical for creators and platforms alike to develop mechanisms for accountability and transparency, ensuring ethical usage of such powerful tools.


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