  LLMs like GPT are great at tasks involving English which dominates in their
training data. In this paper, we look at how they cope with tasks involving
languages that are severely under-represented in their training data, in the
context of data-to-text generation for Irish, Maltese, Welsh and Breton. During
the prompt-engineering phase we tested a range of prompt types and formats on
GPT-3.5 and~4 with a small sample of example input/output pairs. We then fully
evaluated the two most promising prompts in two scenarios: (i) direct
generation into the under-resourced language, and (ii) generation into English
followed by translation into the under-resourced language. We find that
few-shot prompting works better for direct generation into under-resourced
languages, but that the difference disappears when pivoting via English. The
few-shot + translation system variants were submitted to the WebNLG 2023 shared
task where they outperformed competitor systems by substantial margins in all
languages on all metrics. We conclude that good performance on under-resourced
languages can be achieved out-of-the box with state-of-the-art LLMs. However,
our best results (for Welsh) remain well below the lowest ranked English system
at WebNLG'20.
