Machine Translation (MT) is undergoing a paradigm shift, with systems based on fine-tuned large language models (LLM) becoming increasingly competitive with traditional encoder-decoder models trained specifically for translation tasks. However, LLM-based systems are at a higher risk of generating hallucinations, which can severely undermine user’s trust and safety. Most prior research on hallucination mitigation focuses on traditional MT models, with solutions that involve post-hoc mitigation – detecting hallucinated translations and re-translating them. While effective, this approach…
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Mitigating Hallucinated Translations in Large Language Models with Hallucination-focused Preference Optimization
