  The problem of hallucination and omission, a long-standing problem in machine
translation (MT), is more pronounced when a large language model (LLM) is used
in MT because an LLM itself is susceptible to these phenomena. In this work, we
mitigate the problem in an LLM-based MT model by guiding it to better word
alignment. We first study the correlation between word alignment and the
phenomena of hallucination and omission in MT. Then we propose to utilize word
alignment as preference to optimize the LLM-based MT model. The preference data
are constructed by selecting chosen and rejected translations from multiple MT
tools. Subsequently, direct preference optimization is used to optimize the
LLM-based model towards the preference signal. Given the absence of evaluators
specifically designed for hallucination and omission in MT, we further propose
selecting hard instances and utilizing GPT-4 to directly evaluate the
performance of the models in mitigating these issues. We verify the rationality
of these designed evaluation methods by experiments, followed by extensive
results demonstrating the effectiveness of word alignment-based preference
optimization to mitigate hallucination and omission.
