19 citations · 29 across the 5 of their papers we have counts for
6 papers
LABBench2: An Improved Benchmark for AI Systems Performing Biology Research
Jon M Laurent, Albert Bou, Michael Pieler +9
Optimism for accelerating scientific discovery with AI continues to grow. Current applications of AI in scientific research range from training dedicated foundation models on scien…
ChemPile: A 250GB Diverse and Curated Dataset for Chemical Foundation Models
Adrian Mirza, Nawaf Alampara, Martiño Ríos-García +12
Foundation models have shown remarkable success across scientific domains, yet their impact in chemistry remains limited due to the absence of diverse, large-scale, high-quality da…
Arabic Stable LM: Adapting Stable LM 2 1.6B to Arabic
Zaid Alyafeai, Michael Pieler, Hannah Teufel +8
Large Language Models (LLMs) have shown impressive results in multiple domains of natural language processing (NLP) but are mainly focused on the English language. Recently, more L…
Rephrasing natural text data with different languages and quality levels for Large Language Model pre-training
Michael Pieler, Marco Bellagente, Hannah Teufel +9
Recently published work on rephrasing natural text data for pre-training LLMs has shown promising results when combining the original dataset with the synthetically rephrased data.…
Are large language models superhuman chemists?
Adrian Mirza, Nawaf Alampara, Sreekanth Kunchapu +32
Large language models (LLMs) have gained widespread interest due to their ability to process human language and perform tasks on which they have not been explicitly trained. Howeve…
Stable LM 2 1.6B Technical Report
Marco Bellagente, Jonathan Tow, Dakota Mahan +16
We introduce StableLM 2 1.6B, the first in a new generation of our language model series. In this technical report, we present in detail the data and training procedure leading to…