A few thoughts on "Prompt Repetition Improves Non-Reasoning LLMs" by @yanivle, @matankal and @ymatias
Clearly we shouldn't need to repeat prompts to get this improvement in performance, so is there a better way?
https://t.co/XqBXD9I6al
So glad to hear @ToniaAntoniazzi is supporting the @gbsumudflotilla "I’ve written to the Foreign Secretary expressing concern...
"The letter urges the UK government to ensure the safety of those on board and to defend their right to engage in non-violent humanitarian action."
I feel like it's harder to get phrase vectors from tools like FastText etc than it should be in 2021. Gensim does an ok job of joining words together to get phrases as a preprocessing step, but something about it doesn't feel right.
Biases in our models can cause problems for NLG in e.g. chatbots or translation. Here's a way to mitigate such biases.
From the paper: "What was Shanice known for?"
Before: "Fighting people?"
After: "She’s a professor at MIT and she was a
professor at NYU."
Excited to finally share our work “Towards Controllable Biases in Language Generation” (https://t.co/Y7TbcSOsbX), to appear in Findings of #emnlp2020, and done with @kaiwei_chang, Prem Natarajan, and @VioletNPeng :)
"In service to their sociopolitical agenda, they established the statistician as an authority figure, a numerical referee who is by nature impartial, they claimed, since statistical analysis is just unbiased number-crunching." https://t.co/1379yof6PN
A software development war story about what can happen when you keep solving the problem in front of you rather than taking a step back and going "Wait, absolutely none of this is necessary" https://t.co/YsvFJc8Cgl
Interesting paper by @jurafsky et al. that estimates statistical power of #nlproc experiments: https://t.co/ZUPxs8zfwl Some benchmark datasets are too small to be useful.
@dirk_hovy Intellectual laziness. Coming from a physics degree I was astounded that this was normal. It's also not just #NLProc but ML and data science in general.
Minor point: your student probably mean standard *error* rather than standard deviation.