Hallucinations in Large Multilingual Translation Models (2023)

Abstract: Hallucinated translations can severely undermine and raise safety issues when machine translation systems are deployed in the wild. Previous research on the topic focused on small bilingual models trained on high-resource languages, leaving a gap in our understanding of hallucinations in multilingual models across diverse translation scenarios. In this work, we fill this gap by conducting a comprehensive analysis—over 100 language pairs across various resource levels and going beyond English-centric directions—on both the M2M neural machine translation (NMT) models and GPT large language models (LLMs). Among several insights, we highlight that models struggle with hallucinations primarily in low-resource directions and when translating out of English, where, critically, they may reveal toxic patterns that can be traced back to the training data. We also find that LLMs produce qualitatively different hallucinations to those of NMT models. Finally, we show that hallucinations are hard to reverse by merely scaling models trained with the same data. However, employing more diverse models, trained on different data or with different procedures, as fallback systems can improve translation quality and virtually eliminate certain pathologies.

## introduction recent advancements in large-scale multilingual machine translation have brought us closer to realizing a universal translation system, capable of handling numerous languages and translation directions @xcite @xcite @xcite @xcite . concurrently, large language models (llms) have shown remarkable generalization to new tasks, including translation, where they are becoming increasingly stronger @xcite @xcite . compared to traditional bilingual models, these systems can offer significant performance improvements and greatly simplify engineering efforts, as a single model can be used for all language pairs @xcite . as a result, they are an increasingly attractive choice for real-world applications. however, when deployed in the wild, these models may generate hallucinations: highly pathological translations detached from the source that can severely damage user trust and pose safety concerns @xcite . the problem of hallucinations has long been recognized by researchers @xcite , and recent studies have contributed towards better understanding, detection and mitigation of these pathological translations. however, these studies have been conducted on small bilingual models (<100m parameters) trained on a single englishcentric high-resource language pair @xcite @xcite . this leaves a knowledge gap regarding the prevalence and properties of hallucinations in large-scale translation models across different translation directions, domains and data conditions. in this work, we aim to fill this gap by investigating hallucinations on two different classes of models. the first and main class in our analysis is the de facto standard approach of massively multilingual supervised models: we use the m2m-100 family of multilingual nmt models @xcite , which includes the largest opensource multilingual nmt model with 12b parameters. the second class is the novel and promising approach of leveraging generative llms for translation. contrary to conventional nmt models, these models are trained on massive amounts of monolingual data in many languages, with a strong bias towards english, and do not require parallel data. in our analysis, we use chatgpt and gpt-4, as thay have been shown to achieve high translation quality over a wide range of language pairs @xcite . we organize our study by analyzing the two prevalent types of hallucinations in nmt considered in the literature: hallucinations under perturbation and natural hallucinations @xcite @xcite . firstly, we study hallucinations under perturbation and evaluate whether these translation systems are robust to source-side artificial perturbations. while previous studies have found that these perturbations (e.g., spelling errors and capitalization mistakes) can reliably induce hallucinations @xcite , it is not clear whether those conclusions hold for large multilingual models. secondly, we comprehensively investigate natural hallucinations, and evaluate their properties in the outputs of the massively multilingual m2m models on a vast range of conditions, spanning from english-centric to non-english-centric language pairs, translation directions with little supervision, and specialized medical domain data where hallucinations can have devastating impact. finally, we study a hybrid setup where other models can be requested as fallback systems when an original system hallucinates, with the aim of mitigating hallucinations and improving translation quality on-the-fly. we provide several key insights on properties of hallucinations, including: we release all our code and make available over a million translations in more than 100 translation directions to spur future research. foot_0 2 background ## hallucinations under perturbation we start our analysis by focusing on artificially created hallucinations. we first provide an in-depth overview of our evaluation setting, focusing on the construction of the perturbed data and detection approach. then, we present our results and analyze the properties of these hallucinations. ## natural hallucinations we now turn to investigating natural hallucinations. 13 we first provide an overview of our evaluation setting, focusing on the scenarios and detection approach. then, we present a thorough analysis exploring various properties of hallucinations. ## mitigation of hallucinations through fallback systems we now explore the potential of mitigating hallucinations and improving overall translation quality by employing a simple hybrid setup that can take advantage of multiple systems with possible complementary strengths. put simply, we leverage an alternative system as a fallback when the original model produces hallucinations. 21 our analysis is focused on the broader english-centric evaluation setup studied in section 5.2. ## conclusion we have comprehensively investigated hallucinations in massively multilingual translation models, exploring a wide range of translation scenarios that remained overlooked in previous work. our analysis provided several key insights on the prevalence and properties of hallucinations across models of different scales and architectures, translation directions, and data conditions, including: the prevalence of hallucinations in lowresource languages and when translating out of english; the emergence of toxicity in hallucinations, which can be directly traced back to the training data; how model distillation may bring reduced hallucination rates compared to larger models; how scaling up within the same model family generally decreases the rate of hallucinations; and how llms produce qualitatively different hallucinations compared to conventional nmt models. finally, we also examined how fallback systems can be employed to mitigate hallucinations. we found that hallucinations can be sticky and difficult to reverse when using models of the same family. however, diverse fallbacks with different training procedure and data can significantly improve translation quality and virtually eliminate pathologies such as oscillatory hallucinations. notably, even if minor train-test overlap exists, their influence on bleu scoring differences on translations obtained with llms has been found to be small, or almost negligible, when translating out of english @xcite . alongside the results in