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As someone who is in the field: papers proposing to solve the context length problem come out every month. Almost none of the solutions stick or work as well as
by arugulum 3y ago
As someone who is in the field: papers proposing to solve the context length problem come out every month. Almost none of the solutions stick or work as well as a dense or mostly dense model.
You'll know when the problem is solved when model after consistently use a method. Until then (and especially if you're not in the field as a researcher), assume that every paper claiming to tackle context length is simply a nice proposal.
- dr_dshiv 3y agoWhat about Meta’s megabyte? Also nice proposal?
- visarga 3y agoYes. Solving context length has been tried in hundreds of different approaches, and yet most LLMs are almost identical to the original one from 2017. Just to name a few families of approaches: Sparse Attention, Hierachical Attention, Global-Local Attention,Sliding Window Attention, Locality sensitive hashing Attention, State space model, EMA gated attention.
- Loquebantur 3y agoI assume, there is a common point of failure? Notably, human working memory isn't great either. Which begs the question (if the comparison is valid) as to whether that limitation might be fundamental.
- visarga 3y agoThe failure mode is that only long context tasks benefit, short ones work fast enough with full attention, and better. It's amazing that OpenAI never used them in any serious LLM even though training costs are huge.