In 2017, researchers at Google published a paper that proposed a novel neural network architecture for sequence modeling. That architecture is known as the Transformer. The Generative Pretrained Transformer (GPT) and Bidirectional Encoder Representations from Transformers (BERT) are the two most well-known types of transformers. In our article “ChatGPT: The Revolutionary Language Model Taking the World by Storm“, we wrote about the advantages of ChatGPT – an AI chatbot developed by OpenAI. ChatGPT is built on top of the GPT-3 family of Large Language Models. In this article, I will describe a few limitations associated with AI Language Models.
The Limitations of AI Language Models
Although the media often portrays transformers as having boundless capabilities, they are not a solution to every problem and have limitations. Below, I will list some of the difficulties related to using transformers.
English is the dominant language in NLP research. Although there are models for other languages, it is more challenging to find pre-trained models for languages that are rare or have low resources. While multilingual transformers are available, they are likely to perform less effectively than transformers explicitly trained on a single language.
Even though transfer learning can significantly decrease the quantity of labeled training data required for our models, it still requires a substantial amount compared to what a human would need to accomplish the same task.
Text data from the internet is the primary source for pretraining transformer models, which results in the imprinting of tendencies present in the data into the models. It is challenging to ensure that these biases are not racist, sexist, or discriminatory.
Lack of transparency
AI language models utilize complicated algorithms that can be challenging for users to understand. The absence of clarity can create difficulties in determining decisions, leading to concerns regarding fairness and accountability.
In conclusion, while large AI language models like those based on GPT and BERT architectures have revolutionized Natural Language Processing, they still have limitations. Even with its limitations, the AI language models’ potential is tremendous, and there is still much to be explored in the field of NLP. However, it is also essential to recognize that human intelligence and judgment remain crucial in areas such as fairness and accountability, where AI language models’ lack of transparency can lead to concerns. Ultimately, AI language models are a tool to enhance human capabilities, not replace them.
Natural Language Processing with Transformers – Building Language Applications with Hugging Face by Lewis Tunstall, Leandro von Werra and Thomas Wolf
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