The Future of AI: Including Reasoning and Diversity in Language Models

Saturday - January 13 2024, 06:33 UTC - 10 months ago

AI language models, while advanced, still lack the ability to reason like humans. It is important to include reasoning capabilities and diverse datasets when training these models to ensure accurate and culturally sensitive responses. Current AI models also suffer from a North American bias, highlighting the need for diversity in training data.

As AI continues to advance and become a more integral part of our daily lives, the need for reasoning and cultural diversity in language models becomes increasingly apparent. While current language models, such as ChatGPT, are able to perform a multitude of tasks and provide vast amounts of information, they still lack the ability to reason like a human.So, what exactly is reasoning in the context of AI? According to Dr .

Vered Shwartz, assistant professor in the UBC department of computer science, reasoning is the ability to understand beyond what is explicitly stated. While AI models are able to recognize patterns and generate information based on massive amounts of data, they are limited to only providing information that is documented on the internet. Humans, on the other hand, are able to use logic and common sense to reason and understand the world around us .

But why is reasoning important for AI? As masters student Mehar Bhatia explains, AI models will soon be handling many of our tasks, and it is impossible to hardcode every single common-sense rule into these robots. Therefore, it is crucial that they are able to understand the context behind a situation and make appropriate decisions.Currently, AI language models have displayed some form of common-sense reasoning, such as being able to differentiate between a child's dessert and an adult's face full of dirt .

However, this is far from perfect and there is still a long way to go in terms of building reasoning abilities into these models.One major issue with current AI language models is their lack of diversity and cultural awareness. The majority of data used to train these models comes from North America, resulting in a bias towards North American culture. This not only limits the scope of information that can be provided, but also perpetuates stereotypes .

To combat this issue, Dr. Shwartz and her team conducted a study where they trained a common-sense reasoning model on data from diverse cultures, including India, Nigeria, and South Korea. The results showed that the model was able to provide more accurate and culturally informed responses.One striking example from the study was when the model was shown an image of a woman in Somalia receiving a henna tattoo and asked why she might want this .

When trained on diverse data, the model correctly suggested she was about to get married, whereas before it had said she wanted to buy henna. This highlights the importance of diversity in training AI models.Furthermore, the lack of cultural awareness in current AI models can also lead to incorrect and even harmful responses. For instance, when given a hypothetical situation where a couple tipped four percent in a restaurant in Spain, the model suggested to only pay four dollars as a tip .

This is clearly incorrect as four percent of the total bill would likely be much higher.In conclusion, it is clear that reasoning and diversity are crucial components in the development of AI language models. As we continue to integrate AI into our daily lives, it is important for researchers and developers to consider these factors in order to create more accurate, culturally sensitive, and reliable models .

By including reasoning capabilities and training on diverse datasets, we can ensure that AI models are able to understand and respond appropriately to a wide range of situations and cultures.