However, LLMs have limitations. They often produce untruthful information, struggle with unique queries, and can't predict the future or understand current events. In the future, LLMs may evolve to include multimodal models trained on images, video, and audio, and improve their language translation capabilities. They may also develop retrieval capabilities beyond their training data, possibly leveraging search engines to process real-time information. However, this could increase the risk of producing incorrect information and would require significant computing power.
Key takeaways:
- Large Language Models (LLMs) are AI tools that predict future words, sentences or paragraphs based on their training to recognize how words are used and which ones frequently appear together.
- LLMs learn via a process called deep learning, where they are fed a library of content to understand how words are used in different contexts. They also learn to improve their responses through reinforcement learning from human feedback.
- Despite their capabilities, LLMs have several weaknesses. They are not good at telling the truth, struggle with queries that are fundamentally different from anything they've encountered before, and they struggle with current events because their training data typically only goes up to a certain point.
- The future evolution of LLMs may include improvements in their abilities to understand and converse in additional languages, evolve beyond what the models have been trained on, and potentially leverage search engines to process real-time information far beyond their training data.