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New framework detects hallucinations in Sinhala-to-English machine translation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel framework to detect pathological hallucinations in Sinhala-to-English neural machine translation. This system utilizes a synthetic dataset of 45,000 samples, created using five corruption strategies and a semantic rescue mechanism. The fine-tuned mDeBERTa-v3 model achieved a token-level F1 score of 0.841, and an ensemble of neural risk scores, sequence log-probabilities, and cross-lingual embeddings further enhanced detection accuracy, with an AUROC of 0.970. The study also revealed significant variations in hallucination rates across eight different NMT systems. AI

IMPACT This research could lead to more reliable machine translation systems, particularly for low-resource languages, by improving the detection and mitigation of translation errors.

RANK_REASON The cluster contains an academic paper detailing a new method for hallucination detection in machine translation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework detects hallucinations in Sinhala-to-English machine translation

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The cluster contains an academic paper detailing a new method for hallucination detection in machine translation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Navam Obeysekara, Nevidu Jayatilleke ·

    Fact over Fiction: Detection of Pathological Hallucinations in Sinhala-to-English Neural Machine Translation

    arXiv:2610.11389v1 Announce Type: new Abstract: Neural Machine Translation (NMT) models, while capable of producing highly fluent outputs, remain vulnerable to hallucinations, which are translations that are natural yet semantically unrelated to the source. This vulnerability is …

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