Mitigating Hallucinated Translations in Large Language Models with Hallucination-focused Preference Optimization

• URL: http://arxiv.org/abs/2501.17295v1
• Date: Tue, 28 Jan 2025 20:58:43 GMT
• Title: Mitigating Hallucinated Translations in Large Language Models with Hallucination-focused Preference Optimization
• Authors: Zilu Tang, Rajen Chatterjee, Sarthak Garg,
• Abstract summary: Machine Translation systems are at a higher risk of generating hallucinations. We propose a method that intrinsically learns to mitigate hallucinations during the model training phase. Our approach reduces hallucinations by 89% on an average across three unseen target languages.
• Score: 1.9204566034368082
• License:
• Abstract: 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 introduces additional complexity in deploying extra tools in production and also increases latency. To address these limitations, we propose a method that intrinsically learns to mitigate hallucinations during the model training phase. Specifically, we introduce a data creation framework to generate hallucination focused preference datasets. Fine-tuning LLMs on these preference datasets reduces the hallucination rate by an average of 96% across five language pairs, while preserving overall translation quality. In a zero-shot setting our approach reduces hallucinations by 89% on an average across three unseen target languages.

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