11 papers
LangMAP: A Language-Adaptive Approach to Tokenization
Clara Meister, Suchir Salhan, Andrzej Szablewski +3
Language-specific tokenizers improve tokenization quality and the downstream performance of models on those languages. However, using such a tokenizer comes at a cost: either a new…
LLMs Contain Multitudes: How Deployment Context Reshapes Model-Level Preferences and Values
Filip Trhlik, Aoife O'Flynn, Angela Yu +2
Large language models (LLMs) are increasingly characterised in recent evaluation work as having stable, model-level preference and value systems. However, accompanying robustness c…
Learning Dynamics of Meta-Learning in Small Model Pretraining
David Demitri Africa, Yuval Weiss, Paula Buttery +1
Large language models are powerful but costly. We ask whether meta-learning can make the pretraining of small language models not only better but also more interpretable. We integr…
What is the Best Sequence Length for BABYLM?
Suchir Salhan, Richard Diehl Martinez, Zébulon Goriely +1
Transformer language models typically operate with a fixed-length context window, which has grown in step with large-scale pretraining datasets. In the BabyLM Challenge, however, m…
BLiSS 1.0: Evaluating Bilingual Learner Competence in Second Language Small Language Models
Yuan Gao, Suchir Salhan, Andrew Caines +2
To bridge the gap between performance-oriented benchmarks and the evaluation of cognitively inspired models, we introduce BLiSS 1.0, a Benchmark of Learner Interlingual Syntactic S…
Meta-Pretraining for Zero-Shot Cross-Lingual Named Entity Recognition in Low-Resource Philippine Languages
David Demitri Africa, Suchir Salhan, Yuval Weiss +2
Named-entity recognition (NER) in low-resource languages is usually tackled by finetuning very large multilingual LMs, an option that is often infeasible in memory- or latency-cons…