pietrolesci.github.io
@pietrolesci.bsky.social who did a fantastic job!
#ACL2025
New paper + @philipwitti.bsky.social
@gregorbachmann.bsky.social :) arxiv.org/abs/2412.15210
We have several papers this year and many from @milanlp.bsky.social are around, come say hi!
Here are all the works I'm involved in ⤵️
#ACL2025 #ACL2025NLP
We have several papers this year and many from @milanlp.bsky.social are around, come say hi!
Here are all the works I'm involved in ⤵️
#ACL2025 #ACL2025NLP
Let’s talk about it and why it matters👇
@aclmeeting.bsky.social #ACL2025 #NLProc
Let’s talk about it and why it matters👇
@aclmeeting.bsky.social #ACL2025 #NLProc
Let’s talk about it and why it matters👇
@aclmeeting.bsky.social #ACL2025 #NLProc
github.com/tpimentelms/...
github.com/tpimentelms/...
🔗 Commit here: openreview.net/group?id=acl...
🗓️ Deadline: May 20, 2025 (AoE)
#ACL2025 #NLProc
🔗 Commit here: openreview.net/group?id=acl...
🗓️ Deadline: May 20, 2025 (AoE)
#ACL2025 #NLProc
I thank my amazing co-authors Clara Meister, Thomas Hofmann, @tpimentel.bsky.social, and my great advisor and co-author @andreasvlachos.bsky.social!
I thank my amazing co-authors Clara Meister, Thomas Hofmann, @tpimentel.bsky.social, and my great advisor and co-author @andreasvlachos.bsky.social!
We’ll be presenting our paper on pre-training stability in language models and the PolyPythias 🧵
🔗 ArXiv: arxiv.org/abs/2503.09543
🤗 PolyPythias: huggingface.co/collections/...
We’ll be presenting our paper on pre-training stability in language models and the PolyPythias 🧵
🔗 ArXiv: arxiv.org/abs/2503.09543
🤗 PolyPythias: huggingface.co/collections/...
@aclmeeting.bsky.social in Vienna 🎉
💡 L2M2 brings together researchers to explore memorization from multiple angles. Whether it's text-only LLMs or Vision-language models, we want to hear from you! 🌍
In our NeurIPS 2024 paper, we introduce RealMLP, a NN with improvements in all areas and meta-learned default parameters.
Some insights about RealMLP and other models on large benchmarks (>200 datasets): 🧵
In our NeurIPS 2024 paper, we introduce RealMLP, a NN with improvements in all areas and meta-learned default parameters.
Some insights about RealMLP and other models on large benchmarks (>200 datasets): 🧵
We got this idea after their cool work on improving Plug and Play with FM: arxiv.org/abs/2410.02423
We got this idea after their cool work on improving Plug and Play with FM: arxiv.org/abs/2410.02423
Bonus: They have evaluations on downstream benchmarks!
Great work! 🚀
We propose a methodology to approach these questions by showing that we can predict the performance across datasets and losses with simple shifted power law fits.
Bonus: They have evaluations on downstream benchmarks!
Great work! 🚀