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 this https URL

我们提出 LINGOLLM，一种无需训练的方法，使大型语言模型能够处理在其预训练中几乎不出现的未知语言，从而大大提高了翻译能力。

雇佣一位语言学家！：通过上下文语言描述学习濒危语言

Hire a Linguist!: Learning Endangered Languages with In-Context  Linguistic Descriptions

Simultaneous machine translation (SiMT) outputs translation while reading the
source sentence. Unlike conventional sequence-to-sequence (seq2seq) training,
existing SiMT methods adopt the prefix-to-prefix (prefix2prefix) training,
where the model predicts target tokens based on partial source tokens. However,
the prefix2prefix training diminishes the ability of the model to capture
global information and introduces forced predictions due to the absence of
essential source information. Consequently, it is crucial to bridge the gap
between the prefix2prefix training and seq2seq training to enhance the
translation capability of the SiMT model. In this paper, we propose a novel
method that glances future in curriculum learning to achieve the transition
from the seq2seq training to prefix2prefix training. Specifically, we gradually
reduce the available source information from the whole sentence to the prefix
corresponding to that latency. Our method is applicable to a wide range of SiMT
methods and experiments demonstrate that our method outperforms strong
baselines.

该研究提出了一种新方法，通过课程学习以逐步减少可用的源信息，从而实现从序列到序列训练到前缀到前缀训练的过渡，以增强同步机器翻译模型的翻译能力。实验证明，该方法优于强基准模型。