Progress in AI continues to outpace benchmarks.
Check out this new plot, inspired by @DynabenchAI, that shows just how quickly it's happening.
Read more about it here: contextual.ai/plotting-progr…
Today, Meta researchers together with @MLCommons working group, are launching DataPerf, the first platform for building data & data-centric AI algorithm leaderboards.
We're excited for how DataPerf will help to push the data-centric AI field forward ⬇️
Announcing the BabyLM 👶 Challenge,
the shared task at @conll_conf and CMCL'23!
We’re calling on researchers to pre-train language models on (relatively) small datasets inspired by the input given to children learning language.
babylm.github.ioarxiv.org/abs/2301.11796
Ever wanted to know more about generalisation in NLP but overwhelmed with the number of papers on ArXiv? Fear not! We read 400+ papers, 600+ experiments, and designed a taxonomy 📝 to categorise the research for you! (1/n) 🧵
arxiv.org/abs/2210.03050
How can we improve benchmarking? The @DynabenchAI experiment aims to make faster progress with dynamic data collection, and today, we are pleased to introduce our next stage: @metaai has funded 5 exciting research proposals on the theme of "Rethinking Benchmarking”! Congrats to:
"DataPerf: Benchmarks for Data-Centric AI Development"
What if instead of holding the data constant and benchmarking different models, we held the model constant and benchmarked different data pipelines? [1/7]
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