Enhanced Hallucination Detection in Neural Machine Translation through Simple Detector Aggregation

Anas Himmi, Guillaume Staerman, Marine Picot, Pierre Colombo, Nuno M. Guerreiro
2/20/2024
cs.CL

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Abstract

Hallucinated translations pose significant threats and safety concerns when it comes to the practical deployment of machine translation systems. Previous research works have identified that detectors exhibit complementary performance different detectors excel at detecting different types of hallucinations. In this paper, we propose to address the limitations of individual detectors by combining them and introducing a straightforward method for aggregating multiple detectors. Our results demonstrate the efficacy of our aggregated detector, providing a promising step towards evermore reliable machine translation systems.

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Cite this paper

@article{himmi2024enhanced,
  title  = {Enhanced Hallucination Detection in Neural Machine Translation through Simple Detector Aggregation},
  author = {Anas Himmi and Guillaume Staerman and Marine Picot and Pierre Colombo and Nuno M. Guerreiro},
  year   = {2024},
  eprint = {2402.13331},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2402.13331v1}
}

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