---
title: "LLMs Out-of-the-Box Do Not Generate — Shabaeva 2026"
description: "The paper asks whether large language models choose Russian word order according to discourse context."
authors: ["Alina Shabaeva", "John Frederick Bailyn", "Owen Rambow"]
year: 2026
venue: "Proceedings of the 27th Annual Meeting of the Special Interest Group on Discourse and Dialogue"
source: "https://aclanthology.org/2026.sigdial-1.32/"
doi: "10.18653/v1/2026.sigdial-1.32"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/shabaeva-2026-llms-out-box-do-not/"
---
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# LLMs Out-of-the-Box Do Not Generate Context-Appropriate Word Order in Russian

**Authors:** Alina Shabaeva, John Frederick Bailyn, [Owen Rambow](https://md.research.elamur.ai/free/en/authors/owen-rambow/) · **Year:** 2026 · **Venue:** Proceedings of the 27th Annual Meeting of the Special Interest Group on Discourse and Dialogue · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.sigdial-1.32/) · [PDF](https://aclanthology.org/2026.sigdial-1.32.pdf) · [DOI 10.18653/v1/2026.sigdial-1.32](https://doi.org/10.18653/v1/2026.sigdial-1.32) · [OpenAlex](https://openalex.org/W7214047224)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
The paper asks whether large language models choose Russian word order according to discourse context. Russian allows all six orderings of subject, verb and object, and the choice reflects information structure. The authors built a new corpus from Russian translations of American film scripts in the Movie Scripts dataset. They manually labeled content types such as speech and camera directions, then extracted transitive declarative sentences spoken by characters using Stanza. Each sentence was enriched with morphosyntactic, phonological, semantic and discourse features, plus the two preceding speech sentences as context. Traditional classifiers trained on these features predicted word order reasonably well. In contrast, GPT-3.5-turbo and GPT-4o-mini, with zero-shot or few-shot prompts, mostly produced canonical SVO, even though their Russian was largely grammatical. Fine-tuning GPT-4o-mini, or giving it explicit order hints from a classical model, partly closed this gap. The takeaway is that these LLMs have learned Russian grammar but not the pragmatics of word order. This matters for dialogue generation in free word order languages such as Ukrainian.

## Key points
- **Corpus:** 10 of the 20 scripts in the Movie Scripts dataset (Lisovsky) were annotated in INCEpTION, yielding 1,543 target sentences.
  - Inter-annotator agreement reached Cohen's kappa 0.87 (92.6% raw agreement).
  - The Stanza-derived word order labels matched a manual check on 100 sentences with 99% agreement.
- **Gold distribution:** SVO 74.0%, SOV 18.1%, OSV 4.6%, OVS 3.3%.
- **Default prompting:** baseline prompts pushed SVO to 83.6% (GPT-3.5-turbo) and 90.1% (GPT-4o-mini). Few-shot GPT-4o-mini rose even higher, to 95.5% SVO.
- **Best results:**
  - Fine-tuned GPT-4o-mini produced 71.7% SVO, with a more corpus-like spread of other orders, and the top accuracy of 73.57%.
  - GPT-4o-mini given DT word order suggestions came a close second at 73.36%.
- **Release and cost:** the corpus and code are public at github.com/alinashabaeva/word_order_modeling. All API experiments cost roughly US$30.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/)

Topics: [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{shabaeva2026llms,
  title = {{LLMs Out-of-the-Box Do Not Generate Context-Appropriate Word Order in Russian}},
  author = {Shabaeva, Alina and Bailyn, John Frederick and Rambow, Owen},
  booktitle = {Proceedings of the 27th Annual Meeting of the Special Interest Group on Discourse and Dialogue},
  year = {2026},
  doi = {10.18653/v1/2026.sigdial-1.32},
  url = {https://doi.org/10.18653/v1/2026.sigdial-1.32}
}
```

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