From the 1 of 5 linked papers with an AI index.
5 papers
Cracks in the Foundation: Seemingly Minor Architectural Choices Impact Long Context Extension
Amanda Bertsch, Luca Soldaini, Matthew R. Gormley +4
One might imagine that architectural variations within the dense transformer paradigm have a limited effect on accuracy. However, we demonstrate that this is not the case in the lo…
Pretraining Data Can Be Poisoned through Computational Propaganda
Victoria Graf, Hannaneh Hajishirzi, Noah A. Smith +2
The paper shows that language model pretraining data can be poisoned through publicly editable web discussion pages, and introduces a method called HalfLife to estimate how much ma…
Olmix: A Framework for Data Mixing Throughout LM Development
Mayee F. Chen, Tyler Murray, David Heineman +5
Data mixing -- determining the ratios of data from different domains -- is a first-order concern for training language models (LMs). While existing mixing methods show promise, the…
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…