11 citations · 40 across the 16 of their papers we have counts for
13 papers · 1 filter
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…
Fluid Language Model Benchmarking
Valentin Hofmann, David Heineman, Ian Magnusson +7
Language model (LM) benchmarking faces several challenges: comprehensive evaluations are costly, benchmarks often fail to measure the intended capabilities, and evaluation quality…
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…
Scalable Data Ablation Approximations for Language Models through Modular Training and Merging
Clara Na, Ian Magnusson, Ananya Harsh Jha +4
Training data compositions for Large Language Models (LLMs) can significantly affect their downstream performance. However, a thorough data ablation study exploring large sets of c…
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…
Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research
Luca Soldaini, Rodney Kinney, Akshita Bhagia +33
Information about pretraining corpora used to train the current best-performing language models is seldom discussed: commercial models rarely detail their data, and even open model…
Catwalk: A Unified Language Model Evaluation Framework for Many Datasets
Dirk Groeneveld, Anas Awadalla, Iz Beltagy +7
The success of large language models has shifted the evaluation paradigms in natural language processing (NLP). The community's interest has drifted towards comparing NLP models ac…
Paloma: A Benchmark for Evaluating Language Model Fit
Ian Magnusson, Akshita Bhagia, Valentin Hofmann +13
Evaluations of language models (LMs) commonly report perplexity on monolithic data held out from training. Implicitly or explicitly, this data is composed of domains--varying distr…
What's In My Big Data?
Yanai Elazar, Akshita Bhagia, Ian Magnusson +10
Large text corpora are the backbone of language models. However, we have a limited understanding of the content of these corpora, including general statistics, quality, social fact…
Reproducibility in NLP: What Have We Learned from the Checklist?
Ian Magnusson, Noah A. Smith, Jesse Dodge
Scientific progress in NLP rests on the reproducibility of researchers' claims. The *CL conferences created the NLP Reproducibility Checklist in 2020 to be completed by authors at…
Just-DREAM-about-it: Figurative Language Understanding with DREAM-FLUTE
Yuling Gu, Yao Fu, Valentina Pyatkin +3
Figurative language (e.g., "he flew like the wind") is challenging to understand, as it is hard to tell what implicit information is being conveyed from the surface form alone. We…
Exploring The Landscape of Distributional Robustness for Question Answering Models
Anas Awadalla, Mitchell Wortsman, Gabriel Ilharco +4
We conduct a large empirical evaluation to investigate the landscape of distributional robustness in question answering. Our investigation spans over 350 models and 16 question ans…
Extracting Fine-Grained Knowledge Graphs of Scientific Claims: Dataset and Transformer-Based Results
Ian H. Magnusson, Scott E. Friedman
Recent transformer-based approaches demonstrate promising results on relational scientific information extraction. Existing datasets focus on high-level description of how research…