Hire a Linguist!: Learning Endangered Languages with In-Context
  Linguistic Descriptions
Kexun Zhang Yee Man Choi Zhenqiao Song Taiqi He William Yang Wang Lei Li
How can large language models (LLMs) process and translate endangered
languages? Many languages lack a large corpus to train a decent LLM; therefore
existing LLMs rarely perform well in unseen, endangered languages. On the
contrary, we observe that 2000 endangered languages, though without a large
corpus, have a grammar book or a dictionary. We propose LINGOLLM, a
training-free approach to enable an LLM to process unseen languages that hardly
occur in its pre-training. Our key insight is to demonstrate linguistic
knowledge of an unseen language in an LLM's prompt, including a dictionary, a
grammar book, and morphologically analyzed input text. We implement LINGOLLM on
top of two models, GPT-4 and Mixtral, and evaluate their performance on 5 tasks
across 8 endangered or low-resource languages. Our results show that LINGOLLM
elevates translation capability from GPT-4's 0 to 10.5 BLEU for 10 language
directions. Our findings demonstrate the tremendous value of linguistic
knowledge in the age of LLMs for endangered languages. Our data, code, and
model generations can be found at https://github.com/LLiLab/llm4endangeredlang.