Papers › Dynamic Evaluation of Transformer Language Models
Dynamic Evaluation of Transformer Language Models
Ben Krause, Emmanuel Kahembwe, Iain Murray, Steve Renals
This research note combines two methods that have recently improved the state of the art in language modeling: Transformers and dynamic evaluation. Transformers use stacked layers of self-attention that allow them to capture long range dependencies in sequential data. Dynamic evaluation fits models to the recent sequence history, allowing them to assign higher probabilities to re-occurring sequential patterns. By applying dynamic evaluation to Transformer-XL models, we improve the state of the art on enwik8 from 0.99 to 0.94 bits/char, text8 from 1.08 to 1.04 bits/char, and WikiText-103 from 18.3 to 16.4 perplexity points.
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Code
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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 | Hutter Prize | Transformer-XL + RMS dynamic eval | Bit per Character (BPC) | 0.94 | #1 of 18 | Archive leaderboard | report |
| Language Modelling | Hutter Prize | Transformer-XL + RMS dynamic eval | Number of params | 277M | #1 of 18 | Archive leaderboard | report |
| Language Modelling | Text8 | Transformer-XL + RMS dynamic eval + decay | Bit per Character (BPC) | 1.038 | #3 of 24 | Archive leaderboard | report |
| Language Modelling | Text8 | Transformer-XL + RMS dynamic eval + decay | Number of params | 277M | #3 of 24 | Archive leaderboard | report |
| Language Modelling | WikiText-103 | Transformer-XL (RMS dynamic eval) | Number of params | 257M | #13 of 89 | Archive leaderboard | report |
| Language Modelling | WikiText-103 | Transformer-XL (RMS dynamic eval) | Test perplexity | 16.4 | #13 of 89 | Archive leaderboard | report |
| Language Modelling | WikiText-103 | Transformer-XL (RMS dynamic eval) | Validation perplexity | 15.8 | #13 of 89 | Archive leaderboard | report |
| Language Modelling | WikiText-103 | Transformer-XL (SGD dynamic eval) | Number of params | 257M | #19 of 89 | Archive leaderboard | report |
| Language Modelling | WikiText-103 | Transformer-XL (SGD dynamic eval) | Test perplexity | 17.0 | #19 of 89 | Archive leaderboard | report |
| Language Modelling | WikiText-103 | Transformer-XL (SGD dynamic eval) | Validation perplexity | 16.3 | #19 of 89 | Archive leaderboard | report |
| Language Modelling | enwik8 | Transformer-XL (24 layers, RMS dynamic eval, decay) | Bit per Character (BPC) | 0.940 | #2 of 42 | Archive leaderboard | report |
| Language Modelling | enwik8 | Transformer-XL (24 layers, RMS dynamic eval, decay) | Number of params | 277M | #2 of 42 | 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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