Accepted to a Workshop (1/2):

"Self-Retrieval from Distant Contexts for Document-Level Machine Translation", accepted to the Conference on Machine Translation (WMT25), from @ziqianpeng.bsky.social, @rachelbawden.bsky.social, @yvofr.bsky.social

There's many directions where this could go, multilingual, low-resource language, interpretability, depending on your profile, and the internship may lead to a PhD, provided we get funding!

As we found in aclanthology.org/2025.coling-... that BPE-based LLMs (i.e. pretty much all LLMs) did not handle prefixations well

Basically the idea is to extend www.pnas.org/doi/10.1073/... to see how well LLMs model competition between affixes, not only suffixes (e.g. -ity vs. -ness) but also prefixes (e.g. un- vs. non-)

now on arxiv arxiv.org/abs/2510.20508

work done with @yvofr.bsky.social as part of the Democratic Commons programme, many thanks to our colleagues at Make, Sciences Po, and Sorbonne! about.make.org/democratic-c...

the dataset and code are available github.com/PaulLerner/2...

here's what one example of the dataset looks like, there are 72,234 just like this one (I regret my multimodal days where there were pictures in my papers)

I tried for a pythonic library, have a look at the example notebook colab.research.google.com/github/PaulL...

🤔 ppllm is benchmarked against:
- a vllm-based implementation: 4.15 times faster!
- a naive hugging face implementation, which does not sort texts by length: 4.61 times faster!

🤔 ppllm implements windowed PPL, which allows to compute the PPL of arbitrarily long texts.
It aims to be feature complete for many information-theoretic metrics, including Perplexity (PPL), Surprisal, and bits per character (BPC), and their word-level counterparts.

Work done with Laurène Cave,
@haldaume3.bsky.social, Léo Labat, Gaël Lejeune, Pierre-Antoine Lequeu,
@bpiwowar.bsky.social, Nazanin Shafiabadi and @yvofr.bsky.social, read the paper here talnarchives.atala.org/ateliers/202...
Any feedback is appreciated :)

For the EALM Workshop
"On Assessing the Political Biases of Multilingual Large Language Models" by @lernerp.bsky.social Laurène Cave, @haldaume3.bsky.social Léo Labat, Gaël Lejeune, Pierre-Antoine Lequeu, @bpiwowar.bsky.social Nazanin Shafiabadi and yvofr.bsky.social, collaborated with the STIH lab

@mdlhx.bsky.social PS: I just found out we can actually share projects outside of CNRS plmlatex.math.cnrs.fr/6632958859wh...

CNRS provides plmlatex.math.cnrs.fr that covers most of the features. I guess it's not so complicated to host (the software is open source)