Papers › Need a Small Specialized Language Model? Plan Early!
Need a Small Specialized Language Model? Plan Early!
David Grangier, Angelos Katharopoulos, Pierre Ablin, Awni Hannun
Large language models are versatile tools but are not suitable for small inference budgets. Small models have more efficient inference, but their lower capacity means that their performance can be good only if one limits their scope to a specialized domain. This paper explores how to get good specialized small language models using a large, generic, pretraining set and a limited amount of specialized data. We consider two scenarios, depending on whether (i) one can afford pretraining a model for each specialization task, or (ii) one wants to cheaply adapt a single pretrained model for each task. In the first scenario, we propose an effective solution based on importance sampling: we resample the pretraining set to imitate the specialization data and train a small model on it. In the second scenario, we propose a novel architecture, projected networks (PN). PN is a large network whose parameters can be linearly projected into a small network for specialization. For both scenarios, we demonstrate the empirical effectiveness of our solutions across various domains, training set sizes, and training budgets.
In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
No code repository is listed for this paper in the archive or in Syntology's graph.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Language Modelling | The Pile | Larger Transformer 771M (fine-tuned) | Test perplexity | 10 | #28 of 39 | Archive leaderboard | report |
| Language Modelling | The Pile | Smaller Transformer 126M (fine-tuned) | Test perplexity | 12 | #33 of 39 | Archive leaderboard | report |
| Language Modelling | The Pile | Larger Transformer 771M (pre-trained) | Test perplexity | 28.1 | #37 of 39 | Archive leaderboard | report |
| Language Modelling | The Pile | Smaller Transformer 126M (pre-trained) | Test perplexity | 33 | #39 of 39 | 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
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections