Mitigating Hallucinated Translations in Large Language Models with Hallucination-focused Preference Optimization (2025)

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.

## introduction llms are gaining popularity for various nlp applications, including machine translation. fine-tuning llms for mt has been proven to be highly dataefficient, requiring orders of magnitude less parallel data than large standalone multilingual mt models, while achieving increasingly competitive performance @xcite @xcite . moreover, there is a significant and ongoing effort within the research community to push the performance limits of foundational llms and expand their multilingual capabilities @xcite @xcite . 1 work done during internship at apple despite these advantages, llm-based models are more prone to hallucinations: the models generates information that is inaccurate or entirely fabricated. this issue has lead to a growing research area, focusing on the causes, detection, and mitigation of hallucinations in llms @xcite . in the context of mt, hallucinations manifest as highly pathological translations, which can lead to misunderstandings in conversations, potentially damaging relationships and undermining user trust in the system @xcite . most of the existing research on hallucination mitigation in mt has focused on traditional encodedecoder models, establishing effective post-hoc mitigation strategies @xcite . these strategies first detect whether a translation contains hallucination, and if so, generate and present a mitigated translation to the user. in practical scenarios, using post-hoc mitigation has several drawbacks: i) the need for deploying an additional hallucination detector in production; ii) running the hallucination detector on every translation, which increases cost and latency; and iii) re-running inference if a translation hallucinates (which often much slower than regular inference). to address these issues, we propose a framework that intrinsically integrates hallucination mitigation during the llm development phase, aiming to minimize hallucinations from the outset. specifically, we apply post-hoc mitigation strategies offline on a large-scale monolingual corpus, generating a corpus of model hallucinations alongside their corresponding mitigated translations. we then fine-tune the llm using contrastive preference optimization (cpo) @xcite , guiding the model away from hallucinations. our approach requires no additional humanannotated data, is easily scalable across many language pairs, and is highly effectiveachieving a 96% reduction in hallucination rates across five language pairs without sacrificing general translation quality. it also generalizes well, achieving an average 89% reduction in hallucination rates across three unseen target languages. overall, our main contributions include: ## dataset creation framework one of the techniques for fine-tuning llms for translation is preference optimization @xcite which uses a dataset of triplets, consisting of a source sentence x, its preferred translation y p , and a dispreferred translation y d . preference optimization trains the model to prioritize the generation of preferred set of translations over dispreferred ones. @xcite focus on optimizing general translation quality, and hence in their datasets, y p and y d differ only in quality and do not explicitly consider the notion of hallucination. for instance, both translations could be broadly correct, but one might be preferred over the other due to minor errors or subtle differences in style. to address hallucinations, we develop a framework for automatically creating a hallucination focused preference dataset and propose to fine-tune the llm on this dataset to effectively mitigate hallucination generation. in this dataset, the dispreferred translations contain hallucinations, whereas the preferred translations do not. the set of dispreferred translations are derived from the llm's own generated outputs. this is particularly important as it enables the model to learn from its own errors and correct them. our approach for creating this preference dataset is completely unsupervised and can easily scale to multiple languages without any human annotation. at a high level, the dataset creation process consists of translating monolingual data using the llm and automatically detecting hallucinations (section 2.1) and mitigating them using existing post-hoc methods (section 2.2) ## results we present the comparison between different posthoc mitigation strategies in section 5.1 and main results of our fine-tuned models in section 5. ## conclusion in this work, we presented a framework for mitigating translation hallucinations in large language models (llms). to the best of our knowledge, this is among the first works to demonstrate how to mitigate translation hallucination in llms. in this framework, we propose an unsupervised method to create a hallucination-focused preference dataset, which is easily scalable across multiple languages. fine-tuning llms using this dataset through preference optimization reduces hallucination rates by an average of 96%, while preserving general translation quality. additionally, our method generalizes well in a cross-lingual zero-shot setting, achieving an 89% reduction in hallucination rates across three previously unseen target languages.