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Will transformer derived architectures still be state of the art for language modeling in 2025?

The transformer architecture was introduced in the landmark 2017 machine learning paper Attention is All You Need. Previously, many researchers believed that the attention mechanism was among the most promising research directions for improving sequence-to-sequence models. Writing in 2015, Christopher Olah remarked,

LSTMs were a big step in what we can accomplish with RNNs. It’s natural to wonder: is there another big step? A common opinion among researchers is: “Yes! There is a next step and it’s attention!”

This prediction turned out to be correct. Transformers are generally considered to have unseated LSTM at competitive language modeling, and their central operating principle is using the attention mechanism. Will there be another big jump that unseats the transformer architecture by 2025?

Define a transformer derived architecture as one that is either directly referred to as a "transformer" or otherwise cites the 2017 paper from Vaswani et al. as the chief inspiration for its operation. If the architecture is a mix of at least two component architectures, it is also transformer derived if one of the component architectures is a transformer. If there is any contention in the Metaculus comment section, a strawpoll will be taken on the subreddit /r/machinelearning asking,

Is it accurate to say that [the model in question] is a derivative of the transformer model from Vaswani et al.?

After one week, a majority vote indicates the answer, with a tie indicating the answer "Yes".

Either of these must be true for the question to resolve positively:

  1. A Google Scholar search is completed in December 2025 of the phrase

language model "state of the art"

Take the top 5 papers released during the year of 2025. If at least two of them centrally describe some transformer model achieving state of the art performance during the previous year, then this question resolves positively.

OR

  1. This page about NLP progress has its top entry for the WikiText-2 benchmark describing a transformer derived language model in December 2025.

Otherwise, the question resolves negatively.

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