---
title: "Qomhrá: A Bilingual Irish and English Large Language Model"
description: "The authors build Qomhrá, an open-weight 8B Irish–English model. 34 references, cited by 2 papers here."
authors: ["Joseph McInerney", "Khanh-Tung Tran", "Liam Lonergan", "Neasa Ní Chiaráin", "Ailbhe Ni Chasaide", "Barry Devereux"]
year: 2026
venue: "Proceedings of the Second Workshop on Language Models for Low-Resource Languages (LoResLM 2026)"
source: "https://aclanthology.org/2026.loreslm-1.18/"
doi: "10.18653/v1/2026.loreslm-1.18"
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/mcinerney-2026-qomhra-bilingual-irish-english-large/"
---
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# Qomhrá: A Bilingual Irish and English Large Language Model

**Authors:** Joseph McInerney, [Khanh-Tung Tran](https://md.research.elamur.ai/free/en/authors/khanh-tung-tran/), Liam Lonergan, Neasa Ní Chiaráin, Ailbhe Ni Chasaide and Barry Devereux · **Year:** 2026 · **Venue:** [Proceedings of the Second Workshop on Language Models for Low-Resource Languages (LoResLM 2026)](https://md.research.elamur.ai/free/en/venues/loreslm/) ([Workshop on Language Models for Low-Resource Languages 2026](https://md.research.elamur.ai/free/en/venues/loreslm/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.loreslm-1.18/) · [PDF](https://aclanthology.org/2026.loreslm-1.18.pdf) · [DOI 10.18653/v1/2026.loreslm-1.18](https://doi.org/10.18653/v1/2026.loreslm-1.18) · [OpenAlex](https://openalex.org/W7140089794)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · 2 in our library, of the 149,439 papers whose bibliographies we read

## Summary
Irish has very little language technology support among official EU languages, and permissively licensed labelled data for instruction tuning and alignment is especially scarce. The authors build Qomhrá, an open-weight 8B Irish–English model. It is based on Qwen3-8B and trained by bilingual continued pre-training on about 3.3B characters, which include a deliberately large English share to avoid catastrophic forgetting.

To create supervised data, they first had an L1 speaker, an L2 speaker and a panel of LLM judges compare closed-weight models for Irish generation. The humans favoured Gemini-2.5-Pro, and their rankings differed from those of the LLM judges. Gemini-2.5-Pro was then used to translate Dolly V2 into Irish. It also produced a preference dataset by writing a deliberately good and a deliberately poor Irish version of each response, which matched L1 judgments closely.

Qomhrá outperforms UCCIX, the earlier Irish LLM, with gains reaching 29% for Irish and 44% for English. However, error analysis shows that instruction tuning on translated data weakens its underlying Irish ability. The released data and the synthesis recipe offer a transferable approach for other very low-resource languages.

## Key points
- **Pre-training corpus:** 3.265B characters drawn from UCCIX corpora (CulturaX, Glot500, Gaparacrawl, etc.), a sentence-shuffled subset of the National Corpus of Irish (CNG), the Bible, and 50K English Wikipedia samples.
- **Released resources:** Qomhrá plus an AWQ quantized version, the 30K parallel instruction dataset Dolly-V2-gle, and the 1K Irish DPO dataset LIMA-gle-DPO.
- **Preference data agreement:** the synthetic preferences agreed with the L1 annotator at κ = 0.978 when the accepted and rejected candidates differed strongly. In contrast, L2 speakers and LLM judges did not align with the L1 speaker on harder annotation.
- **Hidden cost of instruction tuning:**
  - On BLEU, cleaning outputs with stop tokens showed that the CPT model translated equally well in both directions, while instruction tuning shifted it toward English.
  - On NQ with relaxed matching, the CPT model scored 28.81 versus 23.38 for the instruct model.
- **Training cost:** about 44 hours on 2 H100 GPUs, estimated at roughly 12.37 kg CO₂.

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

## Institutions

- [Trinity College Dublin](https://md.research.elamur.ai/free/en/universities/trinity-college-dublin/) — University, Dublin, Ireland · 25 works · 1,313 citations
- [University College Cork](https://md.research.elamur.ai/free/en/universities/university-college-cork/) — University, Cork, Ireland · 6 works · 119 citations
- [Queen's University Belfast](https://md.research.elamur.ai/free/en/universities/queen-s-university-belfast/) — University, Belfast, United Kingdom · 6 works · 234 citations

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## Cite

Cite as: Joseph McInerney, Khanh-Tung Tran, Liam Lonergan, Neasa Ní Chiaráin, Ailbhe Ni Chasaide and Barry Devereux. 2026. Qomhrá: A Bilingual Irish and English Large Language Model. Proceedings of the Second Workshop on Language Models for Low-Resource Languages (LoResLM 2026).

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

```bibtex
@inproceedings{mcinerney2026qomhra,
  title = {{Qomhrá: A Bilingual Irish and English Large Language Model}},
  author = {Joseph McInerney, Khanh-Tung Tran, Liam Lonergan, Neasa Ní Chiaráin, Ailbhe Ni Chasaide and Barry Devereux},
  booktitle = {Proceedings of the Second Workshop on Language Models for Low-Resource Languages (LoResLM 2026)},
  year = {2026},
  doi = {10.18653/v1/2026.loreslm-1.18},
  url = {https://doi.org/10.18653/v1/2026.loreslm-1.18}
}
```

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*Updated 2 November 2026*