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.

我们探讨了语言生成任务中，训练数据严重不足的情况下，GPT 等 LLM 模型如何处理爱尔兰语、马耳他语、威尔士语和布列塔尼语等语言。我们测试了多种提示类型和格式，发现少量样本训练对于直接生成不常见语言较为有效，但通过英语进行翻译后的生成差距不大。在 WebNLG 2023 中，我们的系统在所有语言和指标上均显著优于其他竞争系统，表现出令人满意的效果。然而，我们在威尔士语中表现最好的结果仍远低于 WebNLG'20 中最差的英语系统。