Who's Who of AI

LAGoM NLP

99 trust researcher @lagom-nlp.bsky.social · 540 followers
AI research Compute & infrastructure Culture, work & education NLP & language
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Researcher with public evidence across AI research, Compute & infrastructure, Culture, work & education.

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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
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July has been a good month: * Our ACL paper about Wikipedia quality was awarded an SAC highlight (aclanthology.org/2026.acl-lon...) * @colemanhaley.bsky.social joined our lab as a postdoc * Coleman's CoNLL paper about impossible languages won the best paper award (aclanthology…

How Good is Your Wikipedia? Auditing Data Quality for Low-resource and Multilingual NLP aclanthology.org
AI Weekly's analysis →
  • MinHash deduplication removes 28.33% of all non-English Wikipedia articles, mostly from editions known to be dominated by bot-generated content.
  • Cited bot-content shares reach 99% for Cebuano, 90% for Waray and 68% for Swedish Wikipedia, per an Alshahrani et al. 2023 estimate.
  • Language models trained on the filtered Wikipedia largely match or outperform those trained on the raw dumps, with the biggest gains on lower-quality editions.
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View on Bluesky · ♥ 11 ↻ 1 ↩ 1 · 3 from the directory shared this · 102d ago

July has been a good month: * Our ACL paper about Wikipedia quality was awarded an SAC highlight (aclanthology.org/2026.acl-lon...) * @colemanhaley.bsky.social joined our lab as a postdoc * Coleman's CoNLL paper about impossible languages won the best paper award (aclanthology…

When transformers learn “impossible” languages, what do they learn? aclanthology.org
AI Weekly's analysis →
  • Janarthan, Haley and Goldwater train GPT-2 style models on perturbed 'impossible' variants of English and probe them beyond perplexity.
  • On BLiMP minimal pairs the models show only gradual degradation on impossible languages, mediated by information locality.
  • Generation is where the bias lives: the same models produce substantially fewer high-quality sentences at longer lengths.
Read full analysis →
View on Bluesky · ♥ 11 ↻ 1 ↩ 1 · 2 from the directory shared this · 102d ago
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Recent commentary

Pixel-based language models use rendered text instead of tokens. This could address several issues with tokenization, but how does adaptation work for such models? This is what we explore in our #EMNLP2026 paper, using Tibetan as a case study. A thread 🧵

View on Bluesky · ♥ 3 ↻ 0 ↩ 1 · 16d ago
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