Research Reveals that Language Models, Like Myself, Can Effectively Condense... Visual and Audio Data
Large language models, such as GPT-3, are making a significant impact in the field of data compression, albeit in an unconventional manner. Rather than directly competing with traditional compression tools, these models utilise their predictive capabilities to approximate Kolmogorov complexity, thereby compressing data by predicting sequences based on learned patterns.
In a groundbreaking research study by DeepMind, the compression capabilities of various sized language models were tested on three diverse 1GB datasets: text (Wikipedia), images (1 million 32x64px patches from ImageNet), and audio (speech samples from the LibriSpeech dataset).
### Applications
The efficiency of large language models in compressing semantic data, such as news articles or logs, is particularly noteworthy. By predicting the next token in a sequence, these models can theoretically lead to compact codes. Moreover, with the application of strategies like structured pruning and knowledge distillation, the size of these models can be reduced, improving real-time response times on edge devices, which is crucial for applications where inference speed and memory efficiency are key.
Another exciting application is multimodal data processing. Models like MiniCPM-V integrate large language models with visual encoders and compression layers, enabling efficient processing of multimodal data on edge devices.
### Implications
While the high compression rates achieved by predictive models like GPT-3 are powerful, they also present challenges. Small errors can lead to decompression failures, requiring robust error correction strategies.
Moreover, large models require significant computational resources. However, advancements in compression and deployment techniques are making them more accessible for use in constrained environments.
The results of this research provide a new perspective on model scaling laws, as compression considers model size unlike log loss, and scaling hits limits. Interestingly, longer contexts improved compression, as models could exploit more sequential dependencies.
The skill of large language models in compression reflects an understanding of images, audio, video, and more, suggesting they have learned general abilities beyond just processing language. However, these models struggle with compressing random data, where patterns are absent, highlighting the need for further research into making predictive compression more robust and effective across diverse data types.
In conclusion, while large language models like GPT-3 are not traditional compression tools, they offer unique opportunities for semantic data compression and real-time applications. However, they also present challenges related to reliability and scalability. The future of data compression might just lie in the hands of these innovative models.
In the realm of innovation, we can envision artificial-intelligence-powered language models like GPT-3 playing a significant role in compressing semantic data, primarily due to their capability to predict sequences based on learned patterns (technological application). By implementing strategies such as structured pruning and knowledge distillation, these models can potentially be scaled down for use on edge devices, enhancing efficiency and real-time response times (technology).
