  The large language model (LLM) has garnered significant attention due to its
in-context learning mechanisms and emergent capabilities. The research
community has conducted several pilot studies to apply LLMs to machine
translation tasks and evaluate their performance from diverse perspectives.
However, previous research has primarily focused on the LLM itself and has not
explored human intervention in the inference process of LLM. The
characteristics of LLM, such as in-context learning and prompt engineering,
closely mirror human cognitive abilities in language tasks, offering an
intuitive solution for human-in-the-loop generation. In this study, we propose
a human-in-the-loop pipeline that guides LLMs to produce customized outputs
with revision instructions. The pipeline initiates by prompting the LLM to
produce a draft translation, followed by the utilization of automatic retrieval
or human feedback as supervision signals to enhance the LLM's translation
through in-context learning. The human-machine interactions generated in this
pipeline are also stored in an external database to expand the in-context
retrieval database, enabling us to leverage human supervision in an offline
setting. We evaluate the proposed pipeline using GPT-3.5-turbo API on five
domain-specific benchmarks for German-English translation. The results
demonstrate the effectiveness of the pipeline in tailoring in-domain
translations and improving translation performance compared to direct
translation. Additionally, we discuss the results from the following
perspectives: 1) the effectiveness of different in-context retrieval methods;
2) the construction of a retrieval database under low-resource scenarios; 3)
the observed domains differences; 4) the quantitative analysis of linguistic
statistics; and 5) the qualitative analysis of translation cases. The code and
data are available at https://github.com/NLP2CT/HIL-MT/.
