Mastering Language Modeling with HTML

Table of Contents

1. Introduction
2. The Importance of Hypertext in Language Models
3. Pre-training Language Models
4. Prompting Language Models
5. Incorporating HTML in Language Models
6. Benefits of Training on HTML Data
7. Data Filtering and Size Reduction
8. Training a BART Model
9. Modification to the Pre-training Objective
10. Auto Prompting
11. Zero Shot Summarization Using Title Tags
12. Table to Text Generation
13. Zero Shot Classification Using HTML Tags
14. Effectiveness of Representation in Fine-Tuning
15. Computation Savings with Prompts
16. Learning from the Internet: Weak Supervision
17. Conclusion

The Importance of Hypertext and the Pre-training of Language Models

In the digital age, the web has become an indispensable source of information, containing vast amounts of textual content in the form of web pages. Language models have played a crucial role in understanding and generating human language, but they have mostly focused on the text data contained within these web pages while disregarding the underlying HTML structure and formatting. However, recent research has demonstrated that incorporating HTML information can significantly improve the performance of language models.

The Importance of Hypertext in Language Models

Hypertext refers to the underlying structure of web documents, where text content is accompanied by HTML tags that define the layout, formatting, and functionality of the content. This information is intrinsic to web pages and provides valuable contextual cues that can enhance language understanding and generation. By considering the HTML tags alongside the text data, language models can gain a deeper understanding of the content and generate more accurate and contextually Relevant responses.

Pre-training Language Models

Before delving into the specific benefits of incorporating HTML in language models, it is crucial to understand the concept of pre-training. Pre-training involves training language models on a large corpus of data to learn the structure and Patterns of language. Popular pre-training models, such as T5, have traditionally focused solely on the text data, excluding HTML information. However, recent research has challenged this approach by suggesting that including HTML can improve the pre-training process.

Prompting Language Models

Prompting refers to the technique of providing a specific instruction or hint to guide language models in generating desired outputs. Previously, prompting has been done using text-Based cues that explicitly instruct the model on what to generate. However, recent advancements in prompting techniques have shown that incorporating HTML tags as prompts can be highly effective in improving language generation and understanding.

Incorporating HTML in Language Models

The incorporation of HTML in language models involves training the models to process and interpret the HTML tags alongside the text data. This enables the models to understand the structure, formatting, and other features intrinsic to web pages. By considering HTML tags, language models can generate more contextually relevant responses, summarize information more accurately, and improve overall performance in various natural language processing tasks.

Benefits of Training on HTML Data

Training language models on HTML data provides a range of benefits over traditional training methods that focus solely on the text content. By considering HTML tags, language models can better understand the intended structure and formatting of web documents. This understanding enables the models to generate outputs that Align with the original rendering of the content, leading to more accurate and contextually appropriate responses.

Data Filtering and Size Reduction

When training on HTML data, certain preprocessing steps are required to filter out unnecessary information and reduce the dataset size. This involves removing boilerplate code and irrelevant HTML tags that do not contribute to the language understanding task. Despite this filtering process, a substantial amount of HTML information remains, allowing language models to gain valuable Context and improve their performance.

Training a BART Model

To test the effectiveness of incorporating HTML, the researchers trained a BART (Bidirectional and AutoRegressive Transformers) model. BART is a state-of-the-art language model with 400 million parameters and was trained on a large dataset of 23 terabytes of filtered HTML data. The training process involved 330k steps on 256 GPUs, making it a highly complex and computationally intensive task.

Modification to the Pre-training Objective

In addition to training the BART model on HTML data, the researchers made a significant modification to the pre-training objective. Instead of masking out individual tokens, they introduced a hint mechanism that indicated the number of tokens masked out. This modification aimed to provide a more informative and contextual hint to the model, improving its understanding and generation capabilities.

Auto Prompting

Auto prompting is an innovative technique that involves generating HTML code around the text as a form of prompt. By incorporating this auto prompt, which resembles the original syntactic structure of web documents, the model can generate outputs that conform to the intended formatting and layout. This technique further enhances the contextuality and coherence of the model's responses.

Zero Shot Summarization Using Title Tags

One of the primary applications of incorporating HTML in language models is zero-shot summarization. In this task, the model is given a web page without any specific instructions and is expected to generate a summary of the content. By utilizing the information contained in the title HTML tags, the model can achieve state-of-the-art performance in zero-shot summarization, surpassing previous models like Pegasus.

Table to Text Generation

Another task where the inclusion of HTML proves valuable is table-to-text generation. When presented with a table formatted using HTML tags, language models can utilize this structure to generate coherent and accurate text descriptions of the table content. Although the performance may not surpass advanced models like GPT-3, the inclusion of HTML tags still yields impressive results in this task.

Zero Shot Classification Using HTML Tags

Using HTML tags as signals for classification tasks is an interesting application of incorporating HTML in language models. By utilizing class and id tags within the HTML, language models can classify paragraphs based on their topic or relevance to a particular category. Although the details of how paragraphs are labeled remain unclear, this approach shows promise in achieving accurate zero-shot classification using HTML signals.

Effectiveness of Representation in Fine-Tuning

After pre-training, language models undergo a process called fine-tuning, where they are trained to perform specific downstream tasks. The representation learned during pre-training plays a crucial role in the effectiveness of fine-tuning. The researchers demonstrated that models trained on HTML data Show significant advantages in fine-tuning, particularly in natural language inference tests. This suggests that the inclusion of HTML tags enhances the model's understanding and performance across various tasks.

Computation Savings with Prompts

Prompts have been shown to improve the efficiency of fine-tuning by reducing the amount of additional labeled data required. The researchers conducted experiments to determine the computational benefits of using prompts. The results demonstrated that models trained with HTML prompts require fewer additional data points to achieve similar performance compared to models without prompts. This finding highlights the data efficiency achieved by incorporating HTML in the training process.

Learning from the Internet: Weak Supervision

The inclusion of HTML information in language models aligns with the broader concept of learning from the internet. Weak supervision, which involves using minimally labeled or algorithmically labeled data, has proven to be a viable strategy for training models at internet Scale. Incorporating HTML tags as a form of weak supervision provides valuable contextual information and improves the overall performance of language models.

Conclusion

In conclusion, the incorporation of HTML information in language models significantly enhances their understanding and generation capabilities. By considering the underlying hypertext structure of web documents, models can generate more contextually relevant responses, improve summarization and classification tasks, and demonstrate better overall performance. The inclusion of HTML tags in training data and prompts offers valuable insights and benefits for language modeling in the digital age.