  Large language models (LLMs) like ChatGPT have exhibited remarkable abilities
on a wide range of natural language processing~(NLP) tasks, including various
machine translation abilities accomplished during chat. However, these models
are only accessible through restricted APIs, which creates barriers to new
research and advancements in the field. Therefore, we propose ParroT, a
framework to enhance and regulate the translation abilities during chat based
on open-source LLMs (e.g., LLaMA), human-written translation and feedback data.
Specifically, ParroT reformulates translation data into the
instruction-following style, and introduces a "$\mathbf{Hint}$" field for
incorporating extra requirements to regulate the translation process.
Accordingly, we propose three instruction types for finetuning ParroT models,
including translation instruction, contrastive instruction, and error-guided
instruction. Experiments on Flores subsets and WMT22 test sets suggest that
translation instruction improves the translation performance of vanilla LLMs
significantly while error-guided instruction can lead to further improvement,
which demonstrates the importance of learning from low-quality translations
annotated by humans. We also demonstrate the potential of automatic evaluation
tools in providing quality information of translations, when constructing
error-guided instructions for directions that lack human annotation data.
Please refer to our Github project for more implementation details:
https://github.com/wxjiao/ParroT
