The article further explores the emergent abilities of LLMs, such as in-context learning and augmented prompting strategies. It also provides insights on how to adapt LLMs to specific tasks through methods like self-supervised fine-tuning, supervised fine-tuning, and distillation. The piece concludes with a discussion on the drawbacks of LLMs, such as their high cost and tendency to "hallucinate" answers, and highlights the competition between different LLMs like Google's LaMDA and OpenAI's GPT series.
Key takeaways:
- Large Language Models (LLMs) are foundation models that generate or embed text, trained on vast amounts of unstructured data through self-supervised learning. They can perform tasks like sentiment analysis, text categorization, text editing, language translation, information extraction, and summarization.
- LLMs have a history dating back to the 1950s, with significant advancements in the 2010s. BERT, introduced by Google in 2019, was the first breakout large language model. OpenAI's GPT series, including GPT-3 and GPT-4, have since set new standards for LLMs.
- Developers can adapt LLMs to specific tasks through methods like self-supervised fine-tuning, supervised fine-tuning, and distillation. Prompt programming or prompt engineering can also be used to narrow and sharpen LLM responses.
- Despite their capabilities, LLMs have drawbacks, including their large size making them expensive to run and their tendency to "hallucinate" answers. The era of "giant, giant" models may be over, with focus shifting towards making them better in other ways.