Improving Language Model Performance: the Two Frontiers

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Language models are a type of artificial intelligence (AI) that can understand and generate human-like text. They have been used to create a wide range of applications, such as chatbots, machine translation, and text summarization.

There are two main frontiers in language modelling research: scaling up and scaling down. So why does scaling matter?

Scaling language models is important for two main reasons.

First, it can lead to significant improvements in performance. Larger models have been shown to achieve state-of-the-art results on a variety of natural language processing tasks.

Second, scaling can make language models more accessible and practical to use. Smaller models can run on edge devices, such as smartphones and IoT (Internet of Things) devices, which opens up new possibilities for applications.

Scaling Up

The goal of scaling up is to create larger and more powerful language models. This can been achieved by increasing the number of parameters in the model — essentially the weights that the model learns to associate with different words and phrases.

Larger language models have shown impressive improvements in performance, but they also come with challenges. They require more data and computational resources to train, and they can be more difficult to deploy in real-world applications.

A study by Brown et al. (2020) showed that scaling up language models can lead to significant improvements in performance. The study trained a language model with 1.56 trillion parameters, which was the largest language model at the time. The model was able to achieve state-of-the-art performance on a variety of natural language processing tasks, including machine translation, text summarization, and question-answering.

Another study by Radford et al. (2022) trained a language model with 175 billion parameters, which is the largest language model to date. The model was named Jurassic-1 Jumbo, and it was able to achieve even better performance than the 1.56 trillion parameter model.

However, scaling up language models also has its challenges. Larger models require more data and computational resources to train. This can be a major obstacle, especially for researchers and organizations with limited resources. Additionally, larger models can be more difficult to deploy in real-world applications. They may require more powerful hardware and they may take longer to make predictions.

Scaling Down

The goal of scaling down is to create smaller and more efficient language models that can run on edge devices, such as smartphones and IoT devices. This is important because edge devices often have limited resources, such as battery power and processing power.

There are several techniques that can be used to scale down language models, such as model compression, on-device learning, and transfer learning.

Model compression involves reducing the size of a language model without significantly affecting its performance. This can be done by removing redundant parameters, or by using techniques such as knowledge distillation.

Knowledge distillation is a technique where a large language model is used to train a smaller model. The large model is first trained on a large dataset. Then, the smaller model is trained on a subset of the data that was used to train the large model. The smaller model learns from the large model and can achieve similar performance with fewer parameters.

On-device learning involves training a language model directly on an edge device. This can be done by using a technique called federated learning, where the model is trained on data that is distributed across multiple devices.

Transfer learning involves taking a language model that has been trained on a large dataset and fine-tuning it on a smaller dataset. This can be done to improve the performance of the model on a specific task, such as machine translation or text summarization.

The Future of Language Modelling

The field of language modelling is rapidly evolving, and there is no clear consensus on which approach is best. However, it is likely that scaling up and scaling down will continue to be important research areas in the future.

By combining the strengths of both approaches, researchers can create language models that are both powerful and efficient. This will enable us to build new and innovative applications that can improve the way we interact with technology.

Here are some of the potential applications of language models:

• Chatbots that can have natural conversations with humans.
• Machine translation systems that can translate text between languages accurately and fluently.
• Text summarization systems that can automatically summarize long pieces of text into a concise and informative format.
• Question-answering systems that can answer user questions in a comprehensive and informative way.
• Creative writing systems that can generate text that is creative and engaging.

Loopio and Avnio applications

In the case of Loopio and Avnio’s application space, which is proposal management, it could be useful to pursue both scaling up and scaling down of language models, depending on the application.

For example, a feature that automatically summarizes contracts might be beneficial to users. This would save users time and effort, and help them to better understand their contracts, and perhaps even ‘converse’ with them. To ensure the accuracy and performance of this feature, a scaled-up language model might be considered.

Alternatively, if we consider developing a mobile app that can provide users with real-time feedback on their sales pitches, using a scaled-down language model might be the way to go. This feature would intend to help users improve their sales skills and close more deals.

Overall, the possibilities for using language models in Loopio and Avnio’s application space are endless. By carefully considering the specific needs of each application, we can determine the best approach to scaling language models to achieve the desired results.

If you’re interested in joining us, check out the career opportunities available across Loopio’s Engineering, Product, and Design teams.

Sources:

• Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., … & Amodei, D. (2020). Language models are few-shot learners. arXiv preprint arXiv:2005.14165.
• Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
• Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2019). Improving language understanding by generative pre-training.
• Howard, J., Ruder, S., & Dehghani, M. (2020). Universal language model fine-tuning for text classification. arXiv preprint arXiv:2004.13380.