Papers › N-Grammer: Augmenting Transformers with latent n-grams

N-Grammer: Augmenting Transformers with latent n-grams

13 Jul 2022arXiv:2207.06366archive 2025-07-28

Aurko Roy, Rohan Anil, Guangda Lai, Benjamin Lee, Jeffrey Zhao, Shuyuan Zhang, Shibo Wang, Ye Zhang, Shen Wu, Rigel Swavely, Tao, Yu, Phuong Dao, Christopher Fifty, Zhifeng Chen, Yonghui Wu

Transformer models have recently emerged as one of the foundational models in natural language processing, and as a byproduct, there is significant recent interest and investment in scaling these models. However, the training and inference costs of these large Transformer language models are prohibitive, thus necessitating more research in identifying more efficient variants. In this work, we propose a simple yet effective modification to the Transformer architecture inspired by the literature in statistical language modeling, by augmenting the model with n-grams that are constructed from a discrete latent representation of the text sequence. We evaluate our model, the N-Grammer on language modeling on the C4 data-set as well as text classification on the SuperGLUE data-set, and find that it outperforms several strong baselines such as the Transformer and the Primer. We open-source our model for reproducibility purposes in Jax.

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tensorflow/lingvo officialmentioned in papertfApache-2.0 report
yiyixuxu/n-grammer-flax mentioned on GitHubjaxApache-2.0 report

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GetFlops tensorflow/lingvo/lingvo/core/activations.py official repository unverified Apache-2.0 (permissive) · 6af7b11f92a34c6c · report
ImportAllParams tensorflow/lingvo/lingvo/model_imports.py official repository unverified Apache-2.0 (permissive) · 9ec960083b074b30 · report
ImportParams tensorflow/lingvo/lingvo/model_imports.py official repository unverified Apache-2.0 (permissive) · ca4beda520280fb9 · report
stateless_cache_dataset tensorflow/lingvo/lingvo/compat.py official repository unverified Apache-2.0 (permissive) · e1e9fb4c30469b3c · report
stateless_shuffle_dataset tensorflow/lingvo/lingvo/compat.py official repository unverified Apache-2.0 (permissive) · f1ff841d96a18fdc · report
get_bigram_ids yiyixuxu/n-grammer-flax/n_grammer_flax/n_grammer_flax.py community (archive-listed) unverified Apache-2.0 (permissive) · 47af72600eb7ee27 · report

Tasks

Common Sense ReasoningCoreference ResolutionLanguage ModelingLanguage ModellingNatural Language InferenceQuestion AnsweringText ClassificationWord Sense Disambiguation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Common Sense Reasoning ReCoRD N-Grammer 343M EM 28.9 #35 of 45 Archive leaderboard report
Common Sense Reasoning ReCoRD N-Grammer 343M F1 29.9 #35 of 45 Archive leaderboard report
Coreference Resolution Winograd Schema Challenge N-Grammer 343M Accuracy 68.3 #37 of 82 Archive leaderboard report
Language Modelling C4 N-Grammer 343M Perplexity 14.79 #7 of 9 Archive leaderboard report
Language Modelling C4 N-Grammer 288M Perplexity 15.01 #8 of 9 Archive leaderboard report
Natural Language Inference CommitmentBank N-Grammer 343M Accuracy 67.9 #14 of 20 Archive leaderboard report
Natural Language Inference CommitmentBank N-Grammer 343M F1 59.7 #14 of 20 Archive leaderboard report
Natural Language Inference RTE N-Grammer 343M Accuracy 59.2% #73 of 90 Archive leaderboard report
Question Answering BoolQ N-Grammer 343M Accuracy 65 #47 of 65 Archive leaderboard report
Question Answering COPA N-Grammer 343M Accuracy 60.0 #56 of 60 Archive leaderboard report
Question Answering MultiRC N-Grammer 343M EM 11.3 #19 of 30 Archive leaderboard report
Question Answering MultiRC N-Grammer 343M F1 62 #19 of 30 Archive leaderboard report
Word Sense Disambiguation Words in Context N-Grammer 343M Accuracy 56.1 #22 of 37 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Absolute Position EncodingsAdamAttentionBPEDense ConnectionsDepthwise ConvolutionDropoutLabel SmoothingLayer NormalizationLinear LayerMulti-DConv-Head AttentionMulti-Head AttentionPosition-Wise Feed-Forward LayerPrimerResidual ConnectionSoftmaxSquared ReLUTransformer

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