There are some weaknesses and limitations of LLM (Large Language Models), including:
Bias: LLMs can learn and perpetuate biases that exist in the data they are trained on, which can have negative social and ethical implications.
Lack of common sense: LLMs do not have a deep understanding of the world and lack common sense reasoning, which can result in their outputs being nonsensical or not grounded in reality.
Limited interpretability: It can be difficult to interpret how LLMs arrive at their outputs, which can make it challenging to diagnose and fix errors or biases in their outputs.
Overfitting: LLMs can sometimes overfit to the training data, meaning that they memorize specific examples instead of learning general patterns and rules, which can result in poor generalization to new inputs.
Resource-intensive: LLMs are computationally expensive to train and require a large amount of data and computational resources, which can make them inaccessible to smaller organizations or individuals.
Environmental impact: The large computational resources required to train and run LLMs have a significant carbon footprint, contributing to climate change.