Enhanced Hallucination Detection in Neural Machine Translation through Simple Detector Aggregation (2024)

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.

## introduction neural machine translation (nmt) has become the dominant methodology for real-world machine translation applications and production systems. as these systems are deployed in-the-wild for realworld usage, it is ever more important to ensure that they are highly reliable. while nmt systems are known to suffer from various pathologies @xcite , the most severe among them is the generation of translations that are detached from the source content, typically known as hallucinations @xcite . although rare, particularly in high-resource settings, these translations can have dramatic impact on user trust @xcite . as such, researchers have worked on (i) methods to reduce hallucinations either during training-time or even inference time @xcite @xcite , and alternatively, (ii) the development of highly effective on-the-fly hallucination detectors @xcite to flag these translations before they reach end-users. in this paper, we will focus on the latter. one immediate way to approach the problem of hallucination detection is to explore high-quality ex-ternal models that can serve as proxies to measure detachment from the source content, e.g., quality estimation (qe) models such as cometkiwi @xcite , or cross-lingual sentence similarity models like laser @xcite and labse @xcite . intuitively, extremely low-quality translations or translations that are very dissimilar from the source are more likely to be hallucinations. and, indeed, these detectors can perform very effectively as hallucination detectors @xcite . alternatively, another effective approach is to leverage internal model features such as attention maps and sequence log-probability @xcite . the assumption here is that when translation models generate hallucinations, they may reveal anomalous internal patterns that can be highly predictive and useful for detection, e.g., lack of contribution from the source sentence tokens to the generation of the translation @xcite . most importantly, different detectors exhibit complementary properties. for instance, oscillatory hallucinations -translations with anomalous repetitions of phrases or n-grams (raunak et al., 2021) -are readily identified by cometkiwi, while detectors based on low source contribution or sentence dissimilarity struggle in this regard. therefore, there is an inherent trade-off stemming from the diverse anomalies different detectors excel at. in this paper, we address this trade-off by proposing a simple yet highly effective method to aggregate different detectors to leverage their complementary strengths. through experimentation in the two most widely used hallucination detection benchmarks, we show that our method consistently improves detection performance. key contributions are as follows: we release our code and scores to support future research and ensure reproducibility. 1 ## summary of key findings from additional experiments the appendix presents several key findings from our additional experiments. firstly, stare consistently outperforms individual detectors and other aggregation techniques, despite some variance between different runs on the halomi dataset. secondly, we show that quantile transformation offers a robust alternative to min-max normalization by mitigating the impact of outliers and maintaining a uniform distribution, with quantile-stare showing competitive performance to stare. additionally, our comparison with the majority vote baseline, focusing on f1 scores, highlights stare's superior performance. lastly, we analyze the contribution of different metrics to stare's decisions, revealing that external detectors are the most discriminative and significantly enhance performance across both benchmarks.