However, despite these promising results, LLMs are not practical tools for data compression compared to existing models due to their size and speed differences. Classic compression algorithms are compact and fast, while LLMs can be large and slow to run on consumer devices. The researchers also found that while larger LLM models achieve superior compression rates on larger datasets, their performance diminishes on smaller datasets. This suggests that a bigger model is not necessarily better for any kind of task and that compression can serve as an indicator of how well the model learns the information of its dataset.
Key takeaways:
- A recent research paper by Google’s AI subsidiary DeepMind suggests that Large Language Models (LLMs) can be seen as strong data compressors, offering a fresh perspective on the capabilities of these models.
- The researchers found that with slight modifications, LLMs can compress information as effectively, and in some cases, even better than widely used compression algorithms.
- Despite their impressive performance, LLMs are not practical tools for data compression compared to existing models, due to their large size and slow speed.
- The study also provides insight into how scale affects the performance of LLMs, suggesting that bigger models are not necessarily better for any kind of task and that compression can serve as an indicator of how well the model learns the information of its dataset.