用大模型检测翻译中的关键错误，提升跨语言系统安全可信度。
Towards Reliable Machine Translation: Scaling LLMs for Critical Error Detection and Safety
- 用指令微调的大模型检测翻译中的事实扭曲、意图反转等关键错误。
- 模型规模越大、微调越充分，错误检测效果越优，超越传统模型。
- 适合关注AI公平性、安全性和多语种信息可信度的研究者与应用方。
机器翻译在跨语言信息获取、公共政策传播和知识公平分发中至关重要，但事实扭曲、意图反转或偏见性翻译等关键错误会损害多语言系统的可靠性、公平性与安全性。本文研究指令微调大语言模型（LLMs）在检测此类错误上的能力，基于公开数据集评估不同参数规模的模型表现。结果表明，模型规模扩展与适配策略（零样本、少样本、微调）均带来持续改进，显著优于XLM-R和ModernBERT等编码器型基线模型。提升机器翻译中的关键错误检测能力，有助于降低虚假信息、误传和语言伤害风险，尤其在高敏感或低资源场景下，是构建更安全、可信与社会负责的多语言AI系统的必要保障。代码将开源于GitHub。
原文摘要 · Abstract (English)
Machine Translation (MT) plays a pivotal role in cross-lingual information access, public policy communication, and equitable knowledge dissemination. However, critical meaning errors, such as factual distortions, intent reversals, or biased translations, can undermine the reliability, fairness, and safety of multilingual systems. In this work, we explore the capacity of instruction-tuned Large Language Models (LLMs) to detect such critical errors, evaluating models across a range of parameters using the publicly accessible data sets. Our findings show that model scaling and adaptation strategies (zero-shot, few-shot, fine-tuning) yield consistent improvements, outperforming encoder-only baselines like XLM-R and ModernBERT. We argue that improving critical error detection in MT contributes to safer, more trustworthy, and socially accountable information systems by reducing the risk of disinformation, miscommunication, and linguistic harm, especially in high-stakes or underrepresented contexts. This work positions error detection not merely as a technical challenge, but as a necessary safeguard in the pursuit of just and responsible multilingual AI. The code will be made available at GitHub.
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