While large language models (LLMs) pre-trained on massive amounts of unpaired
language data have reached the state-of-the-art in machine translation (MT) of
general domain texts, post-editing (PE) is still required to correct errors and
to enhance term translation quality in specialized domains. In this paper we
present a pilot study of enhancing translation memories (TM) produced by PE
(source segments, machine translations, and reference translations, henceforth
called PE-TM) for the needs of correct and consistent term translation in
technical domains.
We investigate a light-weight two-step scenario where, at inference time, a
human translator marks errors in the first translation step, and in a second
step a few similar examples are extracted from the PE-TM to prompt an LLM. Our
experiment shows that the additional effort of augmenting translations with
human error markings guides the LLM to focus on a correction of the marked
errors, yielding consistent improvements over automatic PE (APE) and MT from
scratch.

在本文中，我们进行了一个试点研究，通过在翻译记忆中标记错误并提取类似的例子来引导大型语言模型（LLMs）在技术领域中进行术语翻译以提高翻译质量。实验证明，通过增加人工标记错误的翻译可以引导 LLM 集中于错误修正，相比于自动后期编辑（APE）和从头开始的机器翻译，有着持续的改进。