A Scaled-Up Empirical Study of Syntactic Language Models
Syntactic language models (SLMs) that generate sentences along with their syntactic tree structures have shown promise in both syntactic generalization and downstream tasks. Prior studies, however, have mostly evaluated smaller models trained on less pretraining data and tested on narrower task suites. In addition, the design space of tree linearization and attention masks remains underexplored. We conduct a large-scale empirical study of 12 controlled configurations---one terminal baseline and 11 SLM variants spanning different linearizations and masks---at scales up to 1B parameters and 100B training tokens, evaluated across 11 downstream tasks and two syntactic benchmarks. The experime...
| Authors | Pengcheng Wang, Zhexuan Zhang, Chao Lou et al. |
|---|---|
| Primary category | cs.CL |
| Published | 12 October 2026 |
| Last updated at source | 12 October 2026 |
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