🌍Introducing BabyBabelLM: A Multilingual Benchmark of Developmentally Plausible Training Data!

LLMs learn from vastly more data than humans ever experience. BabyLM challenges this paradigm by focusing on developmentally plausible data

We extend this effort to 45 new languages!

🚨Modeling Abstention via Selective Help-seeking

LLMs learn to use search tools to answer questions they would otherwise hallucinate on. But can this also teach them what they know vs not?

We introduce MASH that trains LLMs for search and gets abstentions for free!

The talk for our work on Retrospective Learning from Interactions, which will be in ACL (once I figure out how to squeeze it shorter)

Gist: autonomous post-training from conversational signals for LLM bootstrapping ... look ma, no annotations! no hand-holding! 🙌📈🚀

www.youtube.com/watch?v=qW8S...

Close your books, test time!
The evaluation pipelines are out, baselines are released & the challenge is on

There is still time to join and
We are excited to learn from you on pretraining and human-model gaps

*Don't forget to fastEval on checkpoints
github.com/babylm/evalu...
📈🤖🧠
#AI #LLMS