# Enhanced Hallucination Detection in Neural Machine Translation through Simple Detector Aggregation

arXiv: https://arxiv.org/abs/2402.13331 · HTML: https://mlsanity.com/paper/2402.13331/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 25 references that are in the map.
- First posted on arXiv 20 Feb 2024.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [Language-agnostic BERT Sentence Embedding](https://mlsanity.com/paper/2007.01852/) (2020)
- [Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond](https://mlsanity.com/paper/1812.10464/) (2018)
- [Six Challenges for Neural Machine Translation](https://mlsanity.com/paper/1706.03872/) (2017)
- [CometKiwi: IST-Unbabel 2022 Submission for the Quality Estimation Shared Task](https://mlsanity.com/paper/2209.06243/) (2022)
- [On Hallucination and Predictive Uncertainty in Conditional Language Generation](https://mlsanity.com/paper/2103.15025/) (2021)
- [The Curious Case of Hallucinations in Neural Machine Translation](https://mlsanity.com/paper/2104.06683/) (2021)
- [Hallucinations in Large Multilingual Translation Models](https://mlsanity.com/paper/2303.16104/) (2023)
- [Detecting and Mitigating Hallucinations in Machine Translation: Model Internal Workings Alone Do Well, Sentence Similarity Even Better](https://mlsanity.com/paper/2212.08597/) (2022)
- [Towards Opening the Black Box of Neural Machine Translation: Source and Target Interpretations of the Transformer](https://mlsanity.com/paper/2205.11631/) (2022)
- [Mitigating Hallucinations and Off-target Machine Translation with Source-Contrastive and Language-Contrastive Decoding](https://mlsanity.com/paper/2309.07098/) (2023)
- [HalOmi: A Manually Annotated Benchmark for Multilingual Hallucination and Omission Detection in Machine Translation](https://mlsanity.com/paper/2305.11746/) (2023)
- [Rainproof: An Umbrella To Shield Text Generators From Out-Of-Distribution Data](https://mlsanity.com/paper/2212.09171/) (2022)
- [A Pseudo-Metric between Probability Distributions based on Depth-Trimmed Regions](https://mlsanity.com/paper/2103.12711/) (2021)

## Built on by (most cited first, 9 of 9)

- [Uncertainty-Aware Decoding with Minimum Bayes Risk](https://mlsanity.com/paper/2503.05318/) (2025)
- [A Survey of Automatic Hallucination Evaluation on Natural Language Generation](https://mlsanity.com/paper/2404.12041/) (2024)
- [QE4PE: Word-level Quality Estimation for Human Post-Editing](https://mlsanity.com/paper/2503.03044/) (2025)
- [Unsupervised Word-level Quality Estimation for Machine Translation Through the Lens of Annotators (Dis)agreement](https://mlsanity.com/paper/2505.23183/) (2025)
- [M2PO: Multi-Perspective Multi-Pair Preference Optimization for Machine Translation](https://mlsanity.com/paper/2510.13434/) (2025)
- [Improving Out-of-Distribution Detection by Combining Existing Post-hoc Methods](https://mlsanity.com/paper/2407.07135/) (2024)
- [Signature Isolation Forest](https://mlsanity.com/paper/2403.04405/) (2024)
- [MMD-Flagger: Leveraging Maximum Mean Discrepancy to Detect Hallucinations](https://mlsanity.com/paper/2506.01367/) (2025)
- [Training-free Truthfulness Detection via Sparse MLP Value Vectors](https://mlsanity.com/paper/2509.17932/) (2025)

## Abstract (arXiv)

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.