Ultra Compressing Causal Language Models
Table of Contents:
- Introduction
- The Problem with Modern Language Models
- Goal: Decreasing Model Size without Losing Performance
- What is a Language Model?
- Causal Language Modeling
- Masked Language Modeling
- Background on Standard Knowledge Distillation
- Proposed Techniques: Bi-directional Knowledge Distillation and Multi-step Knowledge Distillation
- Motivation behind Multi-step Knowledge Distillation
- Motivation behind Bi-directional Knowledge Distillation
- Data used for Training: Wiki Text 103
- Experiment Results and Analysis
- Perplexity on Wiki Text 103
- Downstream Task Performance
- Conclusion and Future Work
- Demonstration
Introduction
In this article, we will explore the concept of multi-step knowledge distillation (MKD) and its application in compressing causal language models. Language models have proven to be extremely powerful tools in various areas such as speech recognition and machine translation. However, they are often too large and expensive to train and not accessible to smaller companies and individuals. The goal is to decrease the model size without compromising performance.
The Problem with Modern Language Models
Modern language models, especially large ones like GPT-3, are prohibitively expensive to train and run. This makes them inaccessible for most people and impractical to use on mobile devices such as phones and laptops. There is a need to overcome these limitations and make language models more accessible to a wider range of users.
Goal: Decreasing Model Size without Losing Performance
The main objective is to reduce the size of language models without sacrificing their performance. This would make them more accessible and usable for smaller companies and individuals. By employing efficient knowledge distillation techniques, we aim to compress and optimize language models to make them more efficient and cost-effective.
What is a Language Model?
A language model is a model that can predict the probability distribution of a STRING of text. It can be used in two main ways: causal language modeling and masked language modeling. Causal language modeling predicts the next word in a sequence of text, while masked language modeling fills in missing words in a given text. In this article, we will focus on causal language modeling.
Background on Standard Knowledge Distillation
Standard knowledge distillation is a process in which a smaller model is trained using a larger model as a teacher. By learning from the teacher's knowledge and predictions, the student model can improve its performance. The teacher provides not only the correct answer but also additional information about other possible answers, enhancing the student's learning process.
Proposed Techniques: Bi-directional Knowledge Distillation and Multi-step Knowledge Distillation
In this article, we will explore two knowledge distillation techniques: bi-directional knowledge distillation and multi-step knowledge distillation.
Bi-directional knowledge distillation involves training an intermediary model between the teacher and the student. This helps to overcome compatibility issues between the teacher and student models and ensures a more efficient transfer of knowledge. The transfer of knowledge happens through the logits at the end of the model.
Multi-step knowledge distillation aims to address the issue of knowledge transfer between components of the transformer architecture. By training an intermediary model and transferring knowledge step by step, it ensures a more effective and comprehensive learning process.
Motivation behind Multi-step Knowledge Distillation
Standard knowledge distillation techniques make no assumptions about the Type of teacher model used. However, this lack of compatibility between teacher and student models can hinder efficient knowledge transfer. Theoretical and empirical evidence suggests that a large gap in size between teacher and student models can lead to ineffective learning. Multi-step knowledge distillation aims to bridge this gap by training an intermediary model that acts as a teacher assistant.
Motivation behind Bi-directional Knowledge Distillation
Bi-directional knowledge distillation draws inspiration from the idea that integrating both left and right contexts in speech recognition could lead to better performance. By combining the forward and backward models, the bi-directional language model can better predict the target word. This approach has the potential to enhance language models in areas such as speech recognition and natural language processing tasks.
Data used for Training: Wiki Text 103
For our experiments, we used the Wiki Text 103 dataset, which consists of Wikipedia articles. This dataset contains around 100 million tokens and serves as a good benchmark for training and evaluating language models. The experiment involved training models of different sizes, ranging from two-layer transformers to 16-layer transformers.
Experiment Results and Analysis
The evaluation of our models was Based on perplexity, which measures how well a language model predicts a given dataset. We compared the perplexity scores of different models, including standard knowledge distillation, multi-step knowledge distillation, and bi-directional knowledge distillation.
The results showed that multi-step knowledge distillation outperformed standard knowledge distillation in terms of both perplexity scores and downstream task performance. The smaller models produced by multi-step knowledge distillation achieved better performance than their teacher models. On the other HAND, bi-directional knowledge distillation did not Show significant improvement over standard knowledge distillation and even underperformed in some cases.
Conclusion and Future Work
In conclusion, multi-step knowledge distillation proves to be a promising technique for compressing causal language models without compromising performance. It offers better perplexity scores and improved downstream task performance compared to standard knowledge distillation. There is a possibility of scaling up the experiments by using larger datasets and exploring different model configurations, such as varying embedding Dimensions.
Future work could also focus on addressing the challenges and limitations of bi-directional knowledge distillation to make it more effective in transferring knowledge between models.
Demonstration
We conducted a live demonstration of an auto-regressive language model that utilizes the compressed models obtained through multi-step knowledge distillation. The model showed impressive text generation abilities while maintaining a smaller model size.
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FAQ
Q: What is knowledge distillation?
A: Knowledge distillation is a process in which a smaller model is trained using a larger model as a teacher. The teacher transfers its knowledge and predictions to the student, improving its performance.
Q: What is the AdVantage of multi-step knowledge distillation?
A: Multi-step knowledge distillation allows for a more efficient transfer of knowledge by training an intermediary model. This improves compatibility between teacher and student and leads to better performance.
Q: Can bi-directional knowledge distillation improve language models?
A: Our experiments showed that bi-directional knowledge distillation did not provide significant improvements over standard knowledge distillation. Further research and experimentation are required to explore its potential benefits.
Q: What dataset was used for training the models?
A: We used the Wiki Text 103 dataset, which consists of Wikipedia articles. It served as a benchmark for training and evaluating our language models.
Q: How can language models be made more accessible?
A: By compressing and optimizing language models through techniques like multi-step knowledge distillation, we can decrease their size and make them more accessible to individuals and smaller companies.
