activity
20232025
most citedOLMo: Accelerating the Science of Language Models

11 citations · 31 across the 7 of their papers we have counts for

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8 papers · 1 filter

cs.CL2025★ 2 cited

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…

cs.CL2025

Signal and Noise: A Framework for Reducing Uncertainty in Language Model Evaluation

David Heineman, Valentin Hofmann, Ian Magnusson +5

Developing large language models is expensive and involves making decisions with small experiments, typically by evaluating on large, multi-task evaluation suites. In this work, we…

cs.CL2025★ 2 cited

2 OLMo 2 Furious

Team OLMo, Pete Walsh, Luca Soldaini +40

We present OLMo 2, the next generation of our fully open language models. OLMo 2 includes a family of dense autoregressive language models at 7B, 13B and 32B scales with fully rele…

cs.CL2024★ 8 cited

Tulu 3: Pushing Frontiers in Open Language Model Post-Training

Nathan Lambert, Jacob Morrison, Valentina Pyatkin +20

Language model post-training is applied to refine behaviors and unlock new skills across a wide range of recent language models, but open recipes for applying these techniques lag…

cs.CL2024★ 7 cited

OLMoE: Open Mixture-of-Experts Language Models

Niklas Muennighoff, Luca Soldaini, Dirk Groeneveld +21

We introduce OLMoE, a fully open, state-of-the-art language model leveraging sparse Mixture-of-Experts (MoE). OLMoE-1B-7B has 7 billion (B) parameters but uses only 1B per input to…

cs.CL2024

OLMES: A Standard for Language Model Evaluations

Yuling Gu, Oyvind Tafjord, Bailey Kuehl +3

Progress in AI is often demonstrated by new models claiming improved performance on tasks measuring model capabilities. Evaluating language models can be particularly challenging,…

cs.CL2024★ 11 cited

OLMo: Accelerating the Science of Language Models

Dirk Groeneveld, Iz Beltagy, Pete Walsh +40

Language models (LMs) have become ubiquitous in both NLP research and in commercial product offerings. As their commercial importance has surged, the most powerful models have beco…

cs.CL2023★ 1 cited

What Makes it Ok to Set a Fire? Iterative Self-distillation of Contexts and Rationales for Disambiguating Defeasible Social and Moral Situations

Kavel Rao, Liwei Jiang, Valentina Pyatkin +5

Moral or ethical judgments rely heavily on the specific contexts in which they occur. Understanding varying shades of defeasible contextualizations (i.e., additional information th…