Do even the best DNN models understand language?

Do even the best DNN models understand language?

New advances, new excitement

Without any doubt, Deep Neural Networks (DNNs) have brought huge improvements to the NLP world recently. News like an AI model using DNN can write articles like a human or can write code to create a website like a real developer comes to mainstream media frequently. A lot of these achievements would have been surreal if we talked about them just a few years ago.

 

One of the most influential models is Bert (Bidirectional Encoder Representations from Transformers), created by Google in 2018. Google claimed with Bert, they now can understand searches better than ever before. Not stopped there, they even took it further by saying embedding this model to its core search engine(SE) “representing the biggest leap forward in the past five years, and one of the biggest leaps forward in the history of Search”. Impressed by the bold claim, I took my liberty to check how the SE works with a COVID-related inquiry like the one below.

Screenshot of a COVID-related inquiry on Google
Screenshot

 

Figure 1: The Search Engine doesn’t just give out locations where vaccine shots are provided but also suggests who is eligible for getting the shots. This result cannot come from a keyword-based search mechanism. And Yes, so far, the result seems to justify their confident claim.

However, Bert was not the only champion in the game. Another powerful language model which was released more recently has come with its advantages. It was GPT-3. Open AI built the model with 175 billion parameters which were 100 times more parameters than its predecessor GPT-2. Due to this large number of parameters and the extensive dataset it has been trained on, GPT-3 performed impressively on the downstream NLP tasks without fine-tuning. Here is an article from MTI Review written by this gigantic model.

Article form MTI Review written by this gigantic model.
Screenshot of an article on MTI Blog

Figure 2: The italicized part was input they fed the model, served as a prompt. This article talks about a unicorn with such fluent English and a high level of confidence, almost indistinguishable from human writing. I would have been convinced the piece of writing was genuine if I did not know the creature did not exist.

 

Many people were astounded at the text that was produced, and indeed, this speaks to the remarkable effectiveness of the particular computational systems. It seems, for some not-crystal-clear reasons, the models understand language. If that’s true, it would be the first step for AI to think like humans. Unsurprisingly, the media took the news by storm. People started to talk about the societal impacts like workforce replace by AI systems. Some even went further by saying humans might be in danger 😉 But really, are we there yet?

Do the models understand language?

So, are the models that great? Are these models capable of understanding language or are they somewhat gaming the whole system? A series of recent papers claimed that models like BERT don’t understand the language in any meaningful way. One of the reasons for their outstanding results might come from their training and testing datasets.