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Preprints, Working Papers, ... Year : 2024

Enhanced Hallucination Detection in Neural Machine Translation through Simple Detector Aggregation

Abstract

Hallucinated translations pose significant threats and safety concerns when it comes to 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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Dates and versions

hal-04574891 , version 1 (14-05-2024)

Identifiers

  • HAL Id : hal-04574891 , version 1

Cite

Anas Himmi, Guillaume Staerman, Marine Picot, Pierre Colombo, Nuno Guerreiro. Enhanced Hallucination Detection in Neural Machine Translation through Simple Detector Aggregation. 2024. ⟨hal-04574891⟩
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