LAGoM NLP

@lagom-nlp.bsky.social

We are the Leuven AI Group of Multilingual NLP (LAGoM NLP), a research lab at the department of Computer Science at KU Leuven, led by @mdlhx

BPE-knockout just got outperformed by an algorithm that modifies BPE tokenisers in a feedback loop to make them absorb more and more constraints. It doesn't even need more data to do that. It uses the tokeniser itself as a dataset. 🧵

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New EACL paper (with @mdlhx.bsky.social)! We tested if comparing perplexity of parallel data across languages is fair. Turns out: it depends. We show the choice of test set (even with consistent meaning) can flip conclusions about which language is easier to model. Paper: arxiv.org/abs/2601.10580

Form and Meaning in Intrinsic Multilingual Evaluations

Intrinsic evaluation metrics for conditional language models, such as perplexity or bits-per-character, are widely used in both mono- and multilingual settings. These metrics are rather straightforwar...

arxiv.org

When is a language hard to model? Previous research has suggested that morphological complexity both does and does not play a role, but it does so by relating the performance of language models to corpus statistics of words or subword tokens in isolation.

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I’m looking for a postdoc, to start ideally ASAP! The work would be in the EU-funded TrustLLM project, focusing on modularisation and language adaptation of LLMs, tokenization, and evaluation benchmarks for multilingual LLMs. The position would be full-time for 2 years with no teaching obligation.

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