Optimal Transport Method Detects Hallucinations in Neural Machine Translation but Shows Limitations for Summarization
Researchers applied optimal transport (OT) mathematics to detect hallucinations in neural machine translation and abstractive summarization, finding the method works well for translation but falls short for summarization faithfulness. The study analyzed attention patterns across decoder layers of language models, finding that hallucinated translations lack an 'exploratory attention phase' seen in correct outputs, while unfaithful summaries can attend correctly to source text yet still misrepresent it. The findings clarify both the promise and principled boundaries of unsupervised geometric methods for AI reliability detection.
A study accepted to the ICML Mechanistic Interpretability Workshop 2026 investigates whether optimal transport (OT) — a mathematical framework measuring geometric distance between probability distributions — can reliably detect hallucinations in neural machine translation (NMT) and abstractive summarization. Analyzing 3,414 translations from a Fairseq German-English model, researchers found that two OT metrics, Wass-to-Unif and Wass-to-Data, are complementary detectors specialized for different hallucination types, with detection concentrated in decoder layers L1–L4 and layer L5 actually being anti-predictive for subtler hallucinations. A key finding is that hallucinated translations lack an exploratory attention phase present in correct translations from the very first decoding step, offering a mechanistic interpretability insight. When applied to abstractive summarization faithfulness on the AggreFact benchmark, the unsupervised OT detector achieved only 57.2–57.6% balanced accuracy on CNN/XSum datasets, well below the supervised MiniCheck-Flan-T5-L baseline of 69.9–74.3%. The researchers argue this gap is principled rather than incidental: unfaithful summaries can attend correctly to source tokens while still misrepresenting their content, a failure mode that concentration-based OT metrics cannot detect by design. Structural experiments on T5-base confirmed consistent decoder organization across model depth, with Layer 3 showing peak attention concentration and Layer 12 being most critical for generation quality. The authors conclude that OT on cross-attention is a reliable unsupervised detector specifically when the failure mode involves source disengagement, but is fundamentally limited for downstream faithfulness failures.
What's missing
The paper does not evaluate OT detection on languages other than German-English, leaving generalizability to other language pairs open. Additionally, computational cost comparisons between the unsupervised OT approach and supervised baselines like MiniCheck are not discussed.
What different sources said
- arXiv cs.CLCenter
Layer-Resolved Optimal Transport for Hallucination Detection in NMT and Abstractive Summarization
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