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Found 4 papers, 2 papers with code
OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization
To this end, we create OPT-IML Bench: a large benchmark for Instruction Meta-Learning (IML) of 2000 NLP tasks consolidated into task categories from 8 existing benchmarks, and prepare an evaluation framework to measure three types of model generalizations: to tasks from fully held-out categories, to held-out tasks from seen categories, and to held-out instances from seen tasks.
Ranked #26 on
Natural Language Inference
on RTE
Large scale distributed neural network training through online distillation
Two neural networks trained on disjoint subsets of the data can share knowledge by encouraging each model to agree with the predictions the other model would have made.
Regularizing Neural Networks by Penalizing Confident Output Distributions
We systematically explore regularizing neural networks by penalizing low entropy output distributions.
Batch Normalized Recurrent Neural Networks
Recurrent Neural Networks (RNNs) are powerful models for sequential data that have the potential to learn long-term dependencies.
