12 papers
Olmo 3
Team Olmo, :, Allyson Ettinger +66
We introduce Olmo 3, a family of state-of-the-art, fully-open language models at the 7B and 32B parameter scales. Olmo 3 model construction targets long-context reasoning, function…
Extracting Rule-based Descriptions of Attention Features in Transformers
Dan Friedman, Adithya Bhaskar, Alexander Wettig +1
Mechanistic interpretability strives to explain model behavior in terms of bottom-up primitives. The leading paradigm is to express hidden states as a sparse linear combination of…
Cache Me If You Can: How Many KVs Do You Need for Effective Long-Context LMs?
Adithya Bhaskar, Alexander Wettig, Tianyu Gao +2
Language models handle increasingly long contexts for tasks such as book summarization, but this leads to growing memory costs for the key-value (KV) cache. Many prior works have p…
SWE-smith: Scaling Data for Software Engineering Agents
John Yang, Kilian Lieret, Carlos E. Jimenez +7
Despite recent progress in Language Models (LMs) for software engineering, collecting training data remains a significant pain point. Existing datasets are small, with at most 1,00…
Lugha-Llama: Adapting Large Language Models for African Languages
Happy Buzaaba, Alexander Wettig, David Ifeoluwa Adelani +1
Large language models (LLMs) have achieved impressive results in a wide range of natural language applications. However, they often struggle to recognize low-resource languages, in…
Organize the Web: Constructing Domains Enhances Pre-Training Data Curation
Alexander Wettig, Kyle Lo, Sewon Min +3
Modern language models are trained on large, unstructured datasets consisting of trillions of tokens and obtained by crawling the web. The unstructured nature makes it difficult to…