So many warn that evaluating with GPT favors GPT
(or any LLM evaluating itself).
Now it is also shown
Science, not just educated guesses
(Fig: T5, GPT, Bart each prefer their own) https://arxiv.org/abs/2311.09766
#enough2skim #scientivism #NLP #nlproc #GPT #LLM #eval #data
A new benchmark for data 📚
Rather than test if a model is good
This tests whether you can filter data
360 languages
They also share metrics for data redundancy if you want just those
https://arxiv.org/abs/2311.06440
https://github.com/toizzy/
#data #preprocessing #dedup #enough2skim #NLP #NLProc
🤖: Detecting if chatGPT made this text...
It did not
A survey on the (few) datasets and methods to detect it
https://arxiv.org/abs/2309.07689
(not sure why chatGPT and not LLM in general, but NVM)
#enough2skim #NLP #nlproc #chatgpt #LLM #LLMs #AGI
Predictions throughout training, hyperparams and architectures are yet again shown to be on
a small manifold
which means models learn their classifications outputs similarly
https://arxiv.org/abs/2305.01604
Mao ... @pratikac
#MachineLearning #enough2skim
Few-shot learning almost reaches traditional machine translation
https://arxiv.org/abs/2302.01398
#enough2skim #NLProc #neuralEmpty
20 questions can now be played by computers
you probably all know @akinator_team@twitter.com that can guess what you thought about
https://arxiv.org/pdf/2301.08718.pdf
propose the other role
They pick a character and will answer yes or no
(basically, QA over wiki+ tweaks)
