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Found 7 papers, 6 papers with code
Language model developers should report train-test overlap
Overall, we take the position that language model developers should publish train-test overlap statistics and/or training data whenever they report evaluation results on public test sets.
Dancing in Chains: Reconciling Instruction Following and Faithfulness in Language Models
Surprisingly, we find that less is more, as training ReSet with high-quality, yet substantially smaller data (three-fold less) yields superior results.
Nemotron-4 340B Technical Report
We release the Nemotron-4 340B model family, including Nemotron-4-340B-Base, Nemotron-4-340B-Instruct, and Nemotron-4-340B-Reward.
Holistic Evaluation of Language Models
We present Holistic Evaluation of Language Models (HELM) to improve the transparency of language models.
When Do You Need Billions of Words of Pretraining Data?
We adopt four probing methods---classifier probing, information-theoretic probing, unsupervised relative acceptability judgment, and fine-tuning on NLU tasks---and draw learning curves that track the growth of these different measures of linguistic ability with respect to pretraining data volume using the MiniBERTas, a group of RoBERTa models pretrained on 1M, 10M, 100M and 1B words.
Learning Which Features Matter: RoBERTa Acquires a Preference for Linguistic Generalizations (Eventually)
One reason pretraining on self-supervised linguistic tasks is effective is that it teaches models features that are helpful for language understanding.
Latent Tree Learning with Ordered Neurons: What Parses Does It Produce?
Recent latent tree learning models can learn constituency parsing without any exposure to human-annotated tree structures.
