Papers › On the State of the Art of Evaluation in Neural Language Models
On the State of the Art of Evaluation in Neural Language Models
Gábor Melis, Chris Dyer, Phil Blunsom
Ongoing innovations in recurrent neural network architectures have provided a steady influx of apparently state-of-the-art results on language modelling benchmarks. However, these have been evaluated using differing code bases and limited computational resources, which represent uncontrolled sources of experimental variation. We reevaluate several popular architectures and regularisation methods with large-scale automatic black-box hyperparameter tuning and arrive at the somewhat surprising conclusion that standard LSTM architectures, when properly regularised, outperform more recent models. We establish a new state of the art on the Penn Treebank and Wikitext-2 corpora, as well as strong baselines on the Hutter Prize dataset.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Language Modelling | WikiText-2 | Melis et al. (2017) - 1-layer LSTM (tied) | Number of params | 24M | #32 of 38 | Archive leaderboard | report |
| Language Modelling | WikiText-2 | Melis et al. (2017) - 1-layer LSTM (tied) | Test perplexity | 65.9 | #32 of 38 | Archive leaderboard | report |
| Language Modelling | WikiText-2 | Melis et al. (2017) - 1-layer LSTM (tied) | Validation perplexity | 69.3 | #32 of 38 | 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.
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