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# Step-by-step Instructions and a Simple Tabular Output Format Improve the Dependency Parsing Accuracy of LLMs

**Authors:** [Hiroshi Matsuda](https://md.mova.elamur.ai/free/en/docs/papers/authors/hiroshi-matsuda/), Chunpeng Ma, [Masayuki Asahara](https://md.mova.elamur.ai/free/en/docs/papers/authors/masayuki-asahara/) · **Year:** 2025 · **Venue:** Proceedings of the 18th International Conference on Parsing Technologies (IWPT, SyntaxFest 2025) **Link:** [aclanthology.org/2025.iwpt-1.2](https://aclanthology.org/2025.iwpt-1.2/) (ACL Anthology 2025.iwpt-1.2) **License of the paper:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) (ACL Anthology) — [creativecommons.org/licenses/by/4.0](https://creativecommons.org/licenses/by/4.0/)

[**0** citations](https://openalex.org/works?filter=cites:W4417309043) openalex · 2026-10-18[**2** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/matsuda-2025-step-step-instructions-simple-tabular/) of the 140673 papers whose bibliographies we read[Proceedings of the 18th International Conference on Parsing Technologies (IWPT, SyntaxFest 2025)](https://aclanthology.org/volumes/2025.iwpt-1/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/2025.iwpt-1.2/)
- [PDF](https://aclanthology.org/2025.iwpt-1.2.pdf)
- [arXiv 2506.09983](https://arxiv.org/abs/2506.09983)
- [OpenAlex](https://openalex.org/W4417309043)

**Authors (3)**

1. [Hiroshi Matsuda](https://openalex.org/A5066539878) [ORCID](https://orcid.org/0000-0002-6297-9897) · h 64 · 13892 citations · 610 works
2. [Chunpeng Ma](https://openalex.org/A5101805835) [ORCID](https://orcid.org/0000-0002-5454-739X) · h 5 · 79 citations · 20 works
3. [Masayuki Asahara](https://openalex.org/A5002196974) [ORCID](https://orcid.org/0000-0002-5178-7275) · h 20 · 1446 citations · 172 works

Citations and author metrics from openalex (2026-10-18); authors with the same name are not always told apart.

## Summary

The paper asks how to get structurally valid, accurate Universal Dependencies parses out of large language models. Instead of bracketed trees, the model emits a reduced CoNLL-U-like table with only the ID, FORM, UPOS, HEAD and DEPREL columns, which makes non-projective trees easy to express and malformed output easy to repair. The instruction splits the task into ordered steps in a chain-of-thought style: part-of-speech tags first, then heads, then relation labels. Models are fine-tuned with LoRA and evaluated on 17 UD r2.15 treebanks, comparing several proprietary and open models. Splitting the task into steps gives a clear gain over a single-step prompt, and a mid-sized open model with a small number of trainable parameters beats UDPipe 2.0 and Hexatagger in LAS on all 17 treebanks. Structural errors such as cycles or multiple roots are almost absent, and a single multilingual model performs about as well as monolingual ones and transfers reasonably to unseen languages.

## How Mova uses it

- [Typical failures of LLM annotators for UD](https://md.mova.elamur.ai/free/en/docs/en/seeds/errors/llm-annotator-failure-modes/) — cited as evidence in the catalogue of typical LLM annotation failures: formal errors (cycles are rare) and what helps (the UPOS → head → relation step order and tabular output).
- The same seed motivates which checks Mova's rule gates run on LLM-produced UD trees: heads and relations are checked first, because LLMs are better at tags than at trees.
- The general lesson (decompose the parse into ordered steps, constrain the output format) is background for the annotation workflow in `ai/annot`, where the deterministic Rust parser produces a draft and the LLM only corrects it.

## Effectiveness in Mova

Not measured separately. The paper serves as background evidence for one knowledge seed and for the design of the annotation gates; no ablation of the step order or the tabular format has been run in Mova.

---

👨‍🔬💥

Topics: [Dependency parsing](https://md.mova.elamur.ai/free/en/docs/papers/topics/dependency-parsing/) · [Treebanks](https://md.mova.elamur.ai/free/en/docs/papers/topics/treebanks/)

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [arXiv 2506.09983](https://arxiv.org/abs/2506.09983)

```
@inproceedings{matsuda2025step,
  title = {{Step-by-step Instructions and a Simple Tabular Output Format Improve the Dependency Parsing Accuracy of LLMs}},
  author = {Matsuda, Hiroshi and Ma, Chunpeng and Asahara, Masayuki},
  booktitle = {Proceedings of the 18th International Conference on Parsing Technologies (IWPT, SyntaxFest 2025)},
  year = {2025},
  eprint = {2506.09983},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2506.09983}
}
```

- [← previous Annotating Second Language in Universal Dependencies: a Review of Current Practices and Directions for Harmonized Guidelines](https://md.mova.elamur.ai/free/en/docs/papers/masciolini-2025-annotating-second-language-universal-dependencies/)
- [next → Probing the Dynamics of Syntactic Ability Acquisition Throughout LLM Pretraining](https://md.mova.elamur.ai/free/en/docs/papers/matsuda-2026-probing-dynamics-syntactic-ability-acquisition/)
- [↑ back to papers](https://md.mova.elamur.ai/free/en/docs/papers/)

## References

13 references, 4 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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