Prompting Large Language Models with Human Error Markings for Self-Correcting Machine Translation
<|reference_start|>Prompting Large Language Models with Human Error Markings for Self-Correcting Machine Translation: 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.<|reference_end|>
@article{berger2024prompting,
    title={Prompting Large Language Models with Human Error Markings for
  Self-Correcting Machine Translation},
    author={Nathaniel Berger, Stefan Riezler, Miriam Exel, Matthias Huck},
    journal={arXiv preprint arXiv:2406.02267},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.02267},
    primaryClass={cs.CL}
}