Kind of. You still need some data from the "future" to extrapolate it: In the paper, they take an LLM finetuned on 2015 political affiliation data, and add to i

Kind of. You still need some data from the "future" to extrapolate it: In the paper, they take an LLM finetuned on 2015 political affiliation data, and add to it the difference between 2020 and 2015 Twitter data, and show that the performance is better when the new model is asked about 2020 political affiliation. So, the LLM still needs to know about 2020 from somewhere. In a way, you teach it about the task, then separately you teach it about 2020, and this method can combine that to make it solve the task for year 2020.