The article discusses a new model called StructBERT that enhances the performance of BERT, a popular language model for natural language processing tasks. StructBERT modifies the pretraining objectives of BERT by introducing word sentence and sentence structural objectives. These modifications improve the model’s ability to understand word order and inter-sentence relations, resulting in better performance on various NLP tasks.
**Large Language Models, StructBERT – Incorporating Language Structures into Pretraining**
Introduction
Since the introduction of BERT, a language model that has shown impressive results in various NLP tasks, researchers have been trying to enhance its performance by modifying its architecture, increasing training data, and changing the hidden size of layers. In this article, we will discuss StructBERT, a new model that surpasses BERT in top benchmarks by incorporating better learning objectives.
StructBERT – Modifying BERT’s Pretraining Objective
StructBERT focuses on slightly modifying BERT’s pretraining objective to make the model smarter. It introduces two new objectives: word sentence objective and sentence structural objective.
1. Word sentence objective
While BERT is good at guessing masked words, it struggles with reconstructing sentences with shuffled words. StructBERT addresses this by partially shuffling input tokens during the masked language modeling task. This helps the model learn to predict the original order of words.
2. Sentence structural objective
BERT’s next sentence prediction task is relatively simple and doesn’t significantly boost its performance. StructBERT enhances this objective by making the model predict the correct sentence order. By constructing training examples with pairs of sequential sentences, StructBERT learns to identify the correct order of sentences.
Combining Objectives
StructBERT combines the word sentence objective and the sentence structural objective into a final objective. This combination of objectives improves the model’s performance in understanding word order and inter-sentence relations.
Practical Applications and Value
StructBERT outperforms BERT in various NLP tasks, making it a valuable tool for companies looking to leverage AI. By incorporating language structures into pretraining, StructBERT can enhance your company’s language understanding capabilities.
If you want to evolve your company with AI, consider using Large Language Models like StructBERT. It can help you automate customer interactions, redefine your sales processes, and improve customer engagement. To get started with AI, follow these steps:
1. Identify Automation Opportunities: Locate key customer interaction points that can benefit from AI.
2. Define KPIs: Ensure your AI endeavors have measurable impacts on business outcomes.
3. Select an AI Solution: Choose tools that align with your needs and provide customization.
4. Implement Gradually: Start with a pilot, gather data, and expand AI usage judiciously.
For AI KPI management advice and continuous insights into leveraging AI, connect with us at hello@itinai.com or stay tuned on our Telegram channel t.me/itinainews and Twitter @itinaicom.
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List of Useful Links:
- AI Lab in Telegram @aiscrumbot – free consultation
- Large Language Models, StructBERT — Incorporating Language Structures into Pretraining
- Towards Data Science – Medium
- Twitter – @itinaicom
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