mlsanityData to 7 Oct 2026

Enhanced Hallucination Detection in Neural Machine Translation through Simple Detector Aggregation

arXiv 2402.13331 ↗First posted 20 Feb 2024cs.CLcited by 9

Not on the map. Why?

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.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 15 of its 25 references that are in the map. First posted on arXiv 20 Feb 2024.

Builds on

15 of 25 references.

References · 15

  1. 2017Attention Is All You Needcited by 59,932
  2. 2022No Language Left Behind: Scaling Human-Centered Machine Translationcited by 1,290
  3. 2020Language-agnostic BERT Sentence Embeddingcited by 621
  4. 2018Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyondcited by 434
  5. 2017Six Challenges for Neural Machine Translationcited by 334
  6. 2022CometKiwi: IST-Unbabel 2022 Submission for the Quality Estimation Shared Taskcited by 205
  7. 2021On Hallucination and Predictive Uncertainty in Conditional Language Generationcited by 176
  8. 2021The Curious Case of Hallucinations in Neural Machine Translationcited by 161
  9. 2023Hallucinations in Large Multilingual Translation Modelscited by 126
  10. 2022Detecting and Mitigating Hallucinations in Machine Translation: Model Internal Workings Alone Do Well, Sentence Similarity Even Bettercited by 75
  11. 2022Towards Opening the Black Box of Neural Machine Translation: Source and Target Interpretations of the Transformercited by 45
  12. 2023Mitigating Hallucinations and Off-target Machine Translation with Source-Contrastive and Language-Contrastive Decodingcited by 42
3 more
  1. 2023HalOmi: A Manually Annotated Benchmark for Multilingual Hallucination and Omission Detection in Machine Translationcited by 35
  2. 2022Rainproof: An Umbrella To Shield Text Generators From Out-Of-Distribution Datacited by 21
  3. 2021A Pseudo-Metric between Probability Distributions based on Depth-Trimmed Regionscited by 17

Built on by

Cited by year

Citing papers per year: 2024: 3; 2025: 6.

9 papers.

  1. 2025Uncertainty-Aware Decoding with Minimum Bayes Riskcited by 10
  2. 2024A Survey of Automatic Hallucination Evaluation on Natural Language Generationcited by 7
  3. 2025QE4PE: Word-level Quality Estimation for Human Post-Editingcited by 6
  4. 2025Unsupervised Word-level Quality Estimation for Machine Translation Through the Lens of Annotators (Dis)agreementcited by 4
  5. 2025M2PO: Multi-Perspective Multi-Pair Preference Optimization for Machine Translationcited by 3
  6. 2024Improving Out-of-Distribution Detection by Combining Existing Post-hoc Methodscited by 2
  7. 2024Signature Isolation Forestcited by 1
  8. 2025MMD-Flagger: Leveraging Maximum Mean Discrepancy to Detect Hallucinationscited by 0
  9. 2025Training-free Truthfulness Detection via Sparse MLP Value Vectorscited by 0