利用人工编纂词汇在低资源语言中进行文本生成

Text Generation in Low-Resource Languages Using Human-Curated Vocabularies

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研究者提出一种框架，将人工编纂词汇与少量人工撰写的示例句结合，并按 Zipf 分布采样候选词，让 LLM 在极低资源甚至未见过的语言中生成连贯文本。实验按词汇多样性、偏离目标比例和人工流畅度评分评估，结果显示具备推理能力的 LLM 即使在极低资源语言中也能生成流畅文本，句子级表现尤为突出，段落级也有可观效果。

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Abstract:Synthetic text generation offers a promising approach to alleviating data scarcity in low-resource languages. However, its effectiveness is inherently limited for languages that are scarcely represented, or entirely absent, in an LLM's pretraining data. At the same time, many such languages have human-curated vocabularies that provide rich lexical and semantic information, often accompanied by grammatical information such as part-of-speech labels. In this work, we ask whether LLMs can leverage these resources to generate coherent text in extremely low-resource and unseen languages. We introduce a framework that combines human-curated vocabularies with a small number of human-written exemplars that provide syntactic signals, while sampling candidate words according to a Zipfian distribution. Across languages with varying levels of resource availability, we evaluate the generated text using lexical diversity, off-target ratio, and human judgments of fluency. Our results show that reasoning-enabled LLMs can generate fluent text even in extremely low-resource languages, with particularly strong results at the sentence level and promising performance at the paragraph level. Our findings highlight a practical pathway for bootstrapping synthetic data in low-resource languages, expanding the amount of usable text even when existing digital resources are extremely limited. Our source code is available at this https URL.

Subjects:	Computation and Language (cs.CL)
Cite as:	arXiv:2610.37105 [cs.CL]
	(or arXiv:2610.37105v1 [cs.CL] for this version)
	https://doi.org/10.48550/arXiv.2610.37105 arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Michael Christlambert Sinanta [view email]
[v1] Tue, 27 Oct 2026 12:05:40 UTC (1,767 KB)

来源：arXiv cs.CL · arxiv.org