Large Language Models (LLMs) are artificial intelligence systems that can process and understand natural language instructions and produce human-like responses. These models have been gaining popularity in recent years due to their ability to solve a wide range of downstream tasks, such as question answering and multi-step reasoning, with minimal training data.
One of the key advantages of LLMs is their ability to learn from natural language instructions alone, without requiring extensive parameter tuning or optimization. This ability is called in-context learning, which enables users to employ an LLM to extract aspects of products without needing expert knowledge of the e-commerce domain.
However, LLMs also have some limitations. For instance, they demand significant computational resources for model inference and can be slow in responding. Research on LLM knowledge distillation and compression is underway to address these limitations and make LLMs more cost-effective, efficient, and scalable.
Another challenge with LLMs is hallucination, which means the model makes up contents that do not exist in reality or do not satisfy the requirements in the prompt. While hallucination can be useful for creative use cases, it can also be an obstacle for data processing.
In summary, LLMs are powerful AI systems that can process and understand natural language instructions and produce human-like responses. They have the ability to learn from natural language instructions alone, but they also have limitations related to computational resources and hallucination. Research is ongoing to address these limitations and make LLMs more efficient, scalable, and cost-effective.
Scaling Large Language Models: Challenges and Opportunities in Data Engineering
