a text embedding model is used to evaluate deletion candidates & choose the one that least disrupts the "meaning" of the original input
bsky.app/profile/maxk...
given a sentence, Savelost repeatedly picks one letter to delete…
while trying to keep the new sentence as semantically similar as possible to the orig sentence
concept + algo by me, js port + frontend by @barrettrees.bsky.social
barrettrees.com/savelost
a text embedding model is used to evaluate deletion candidates & choose the one that least disrupts the "meaning" of the original input
bsky.app/profile/maxk...
- complex sampling techniques – e.g., beam-searching for diverse strings in token sequence space dl.acm.org/doi/full/10....
- finetuning – e.g., "forcing diffuse distributions" arxiv.org/abs/2404.10859; our recent COLM work arxiv.org/abs/2503.17126
- complex sampling techniques – e.g., beam-searching for diverse strings in token sequence space dl.acm.org/doi/full/10....
- finetuning – e.g., "forcing diffuse distributions" arxiv.org/abs/2404.10859; our recent COLM work arxiv.org/abs/2503.17126
1. ask LLM for list of "dimensions" that stories might vary along (eg tone, genre, protag)
2. ask LLM for list of "values" each dim could take (eg protag -> young boy, old woman…)
3. generate stories with diff value combinations
dl.acm.org/doi/full/10....
1. ask LLM for list of "dimensions" that stories might vary along (eg tone, genre, protag)
2. ask LLM for list of "values" each dim could take (eg protag -> young boy, old woman…)
3. generate stories with diff value combinations
dl.acm.org/doi/full/10....
as always, her harassment was driven by a handful of "target callers": big accounts that pick out targets for a swarm of part-time volunteer harassers
if we want public social media, we have to solve target calling
the answer is pretty much always that people tore them to shreds for something they did not remotely deserve being torn to shreds for, and did it over and over until they decided they were happier offline