“The ability to memorize large databases of facts could have potential ramifications for society, especially if those databases include sensitive personal information or copyrighted works. However, one advantage of using an external memory is that the memory can be easily cleared of all such information”
That’s it? Just ‘may have ramifications’?
No concern that this enables ‘Tay’-like failure modes where a system can be manipulated through input into generating particular output?
Or even just grappling with whether adding ‘memory of experiences’ to a language model might open the door to creating a system that has beliefs, or opinions…? and that maybe there might be some ethical concerns with just wiping that out?
Tldr: as a general rule you can ignore the ethics section of ML papers.
That’s the whole problem that led to the introduction of these sections.
You're doing it wrong then.
Ignoring ethics is lazy.
More generally still, you can ignore the ethics of ML researchers- pretty much for the same reasons that you can ignore the Great Turnip of Justice in the sky.
Isn't that the core idea in prompting and few shot learning for large language models?
Today, we’re seeing big models that can encode all of wikipedia in useful ways. If the encodings are “good enough” then you can encode all of wikipedia once, before training another model that just has to encode a question, then use encoded wikipedia to decode an answer, then do backprop through just the answer and question. If wikipedia changes in the meantime, you can probably just update your database of encoded stuff and your learned QA model will be able to incorporate that new information.
Train on test, improved performance on test. Wow.
Transformers are very limited in the size of the attention window. They can take a few thousand tokens at maximum. But your data might not fit into the window, and you also don't want to have to fine-tune the model. This paper offers a solution.
[0] - https://www.deepmind.com/publications/improving-language-mod...
[1] - https://jalammar.github.io/illustrated-retrieval-transformer...
[2] - https://arsham.substack.com/p/retrieval-transformers-for-med...
