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ARXIV:2604.02135 · LLM EVALUATION · SUBMITTED 03 APR · 20:30 UTC · FRESHNESS STALE
ARXIV:2604.02135LLM EVALUATIONSUBMITTED 03 APR · 20:30 UTCFRESHNESS STALEPeter Devine · William Lamb · Beatrice Alex · Ignatius Ezeani · Dawn Knight · Mícheál J. Ó Meachair · +2 at arXiv
A new benchmark for evaluating LLM performance in Scottish Gaelic, revealing above-human capabilities and identifying key performance gaps.
Opportunity summary
Pain A new benchmark for evaluating LLM performance in Scottish Gaelic, revealing above-human capabilities and identifying key performance gaps.
Evidence 0 refs | 0 sources | 50% coverage
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A new benchmark for evaluating LLM performance in Scottish Gaelic, revealing above-human capabilities and identifying key performance gaps. This is particularly acute for morphosyntactically rich minority languages such as Scottish Gaelic, where translation benchmarks…
Multilingual large language models (LLMs) often exhibit emergent 'shadow' capabilities in languages without official support, yet their performance on these languages remains uneven and under-measured. This is particularly acute for morphosyntactically rich minority languages…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Multilingual large language models (LLMs) often exhibit emergent 'shadow' capabilities in languages without official support, yet their performance on these languages remains uneven and…
LLM Evaluation moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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A new benchmark for evaluating LLM performance in Scottish Gaelic, revealing above-human capabilities and identifying key performance gaps.
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10.48550/arXiv.2604.02135A new benchmark for evaluating LLM performance in Scottish Gaelic, revealing above-human capabilities and identifying key performance gaps.
Abstract
Multilingual large language models (LLMs) often exhibit emergent 'shadow' capabilities in languages without official support, yet their performance on these languages remains uneven and under-measured. This is particularly acute for morphosyntactically rich minority languages such as Scottish Gaelic, where translation benchmarks fail to capture structural competence. We introduce GaelEval, the first multi-dimensional benchmark for Gaelic, comprising: (i) an expert-authored morphosyntactic MCQA task; (ii) a culturally grounded translation benchmark and (iii) a large-scale cultural knowledge Q&A task. Evaluating 19 LLMs against a fluent-speaker human baseline ($n=30$), we find that Gemini 3 Pro Preview achieves $83.3\%$ accuracy on the linguistic task, surpassing the human baseline ($78.1\%$). Proprietary models consistently outperform open-weight systems, and in-language (Gaelic) prompting yields a small but stable advantage (+$2.4\%$). On the cultural task, leading models exceed $90\%$ accuracy, though most systems perform worse under Gaelic prompting and absolute scores are inflated relative to the manual benchmark. Overall, GaelEval reveals that frontier models achieve above-human performance on several dimensions of Gaelic grammar, demonstrates the effect of Gaelic prompting and shows a consistent performance gap favouring proprietary over open-weight models.
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PROBLEM
A new benchmark for evaluating LLM performance in Scottish Gaelic, revealing above-human capabilities and identifying key performance gaps. This is particularly acute for morphosyntactically rich minority languages such as Scottish Gaelic, where translation benchmarks fail to ca...
METHOD
Multilingual large language models (LLMs) often exhibit emergent 'shadow' capabilities in languages without official support, yet their performance on these languages remains uneven and under-measured. This is particularly acute for morphosyntactically rich minority languages su...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Multilingual large language models (LLMs) often exhibit emergent 'shadow' capabilities in languages without official support, yet their performance on these languages remains uneven and under-measured. Co...
WHY NOW
LLM Evaluation moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Gemini 3 Pro Preview achieves $83.3\%$ accuracy on the linguistic task, surpassing the human baseline ($78.1\%$).
Explicitly stated in the abstract with specific numeric results.
partial
Proprietary models consistently outperform open-weight systems
Directly stated in the abstract as a consistent finding.
partial
in-language (Gaelic) prompting yields a small but stable advantage (+$2.4\%$).
Explicitly stated in the abstract with specific numeric improvement.
partial
On the cultural task, leading models exceed $90\%$ accuracy
Directly stated in the abstract with clear numeric threshold.
partial
most systems perform worse under Gaelic prompting
Directly stated in the abstract, though slightly less specific than other claims.
partial
We introduce GaelEval, the first multi-dimensional benchmark for Gaelic
Explicitly stated in the abstract as a novel contribution.
partial
translation benchmarks fail to capture structural competence
Directly stated in the abstract as a limitation of existing approaches.
partial
absolute scores are inflated relative to the manual benchmark
Directly stated in the abstract, though the exact meaning of 'inflated' requires some interpretation.
partial
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A new benchmark for evaluating LLM performance in Scottish Gaelic, revealing above-human capabilities and identifying key performance gaps.
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