1 citations · 1 across the 9 of their papers we have counts for
9 papers
SumTablets: A Transliteration Dataset of Sumerian Tablets
Cole Simmons, Richard Diehl Martinez, Dan Jurafsky
Sumerian transliteration is a conventional system for representing a scholar's interpretation of a tablet in the Latin script. Thanks to visionary digital Assyriology projects such…
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…
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…
Investigating ReLoRA: Effects on the Learning Dynamics of Small Language Models
Yuval Weiss, David Demitri Africa, Paula Buttery +1
Parameter-efficient methods like LoRA have revolutionised large language model (LLM) fine-tuning. ReLoRA extends this idea to pretraining by repeatedly merging and reinitialising l…
Pico: A Modular Framework for Hypothesis-Driven Small Language Model Research
Richard Diehl Martinez, David Demitri Africa, Yuval Weiss +3
Building language models (LMs), especially small and medium ones, remains more art than science. While large LMs often improve by sheer scale, it is still unclear why many design 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…
From Babble to Words: Pre-Training Language Models on Continuous Streams of Phonemes
Zébulon Goriely, Richard Diehl Martinez, Andrew Caines +2
Language models are typically trained on large corpora of text in their default orthographic form. However, this is not the only option; representing data as streams of phonemes ca…
Tending Towards Stability: Convergence Challenges in Small Language Models
Richard Diehl Martinez, Pietro Lesci, Paula Buttery
Increasing the number of parameters in language models is a common strategy to enhance their performance. However, smaller language models remain valuable due to their lower operat…
CLIMB: Curriculum Learning for Infant-inspired Model Building
Richard Diehl Martinez, Zebulon Goriely, Hope McGovern +4
We describe our team's contribution to the STRICT-SMALL track of the BabyLM Challenge. The challenge requires training a language model from scratch using only a relatively small t…