This YouTube video on linguistics in forensics made me think about language and personal identity. Our idiolect shapes our identity, how others see us and the way we ultimately, think. The last part of this sentence reminds me of another blogpost How to think in writing.
Now, when people use these LLMs to write in any context: how does that impact their own idiolect and identity? Personally, I hate LinkedIn and seeing every post on there being written by ChatGPT. I also disdain when I see lesson materials, even at university, being written in the same manner. This obviously has to do not only with the text itself, but also with the amount and care I attribute to the fact that someone uses generative AI in writing.
This blog post makes the case for how outsourcing your thinking via writing with LLMs erodes human relationships.
Many see LLMs as a great boon for helping people express their opinions more clearly, particularly for people not using their native language or those who have learning disabilities. As long as the meaning originates from a person, LLMs can help express that meaning in correct and effective language. I have two main objections against this. The first one is about what happens to the text: In most cases it’s impossible to separate the meaning from the expression of it. That is in essence what language is — the words are the meaning. Changing the phrasing changes the message. The second one is about what happens to us: We rob ourselves of the opportunity to grow and learn, without training wheels. LLMs can certainly help people improve the text, but the thinking process — developing the ideas — will be severely amputated when leaving the phrasing up to an AI model. They quickly become a replacement instead of help, depriving us the opportunity of discovering our own voice and who we can be and become when we stand on our own two feet.
With great care, one may be able to use a chatbot without being affected by these two drawbacks, but the problem is that with LLMs, there is an exceptionally thin line between getting help with spelling or grammar, and having the model essentially write for you, thereby glossing over your own voice. This is unavoidable with the current design of chatbots and LLM-powered tools; the step from old-school autocorrect to a generative language model is far too big. If we really envision LLMs as a tool for helping people become better at writing, we need to have a much more carefully considered interface than the chatbots we have today.
The chatbots may have lowered the threshold for participation, but the competition’s ground rules hasn’t changed. To get better at writing, you need to write. The same goes for thinking. Applying for a job means showing who you are, not who the LLM thinks you are, or should be. Participating in the public debate is having to work out how to express opinions in clear language. Am I really participating if I’m not finding my own words?
It is important to note that not all text is affected in the same way. The category of writing that I like to call “functional text”, which are things like computer code and pure conveyance of information (e.g., recipes, information signs, documentation), is not exposed to the same issues. But text that has a personal author addressing a human audience, has particular role expectations and rests on a particular trust. An erosion of that trust will be a loss for humanity.
A pragmatic attitude would be to just let the inflation of text ensue, and take stock after the dust has settled. What will be left of language afterwards? My conservative viewpoint stems from believing that what we will lose is of greater worth than what we gain. While LLMs can prove useful in the short term, using them is treating a symptom instead of the problem. It is a crutch, although some may truly be in need of that crutch. My only advice would be to make sure you actually need it before you lean on it.