Multilingual LLMs often have knowledge disparities across languages, with
larger gaps in under-resourced languages. Teaching LLMs to abstain in the face
of knowledge gaps is thus a promising strategy to mitigate hallucinations in
multilingual settings. However, previous studies on LLM abstention primarily
focus on English; we find that directly applying existing solutions beyond
English results in up to 20.5% performance gaps between high and low-resource
languages, potentially due to LLMs' drop in calibration and reasoning beyond a
few resource-rich languages. To this end, we propose strategies to enhance LLM
abstention by learning from multilingual feedback, where LLMs self-reflect on
proposed answers in one language by generating multiple feedback items in
related languages: we show that this helps identifying the knowledge gaps
across diverse languages, cultures, and communities. Extensive experiments
demonstrate that our multilingual feedback approach outperforms various strong
baselines, achieving up to 9.2% improvement for low-resource languages across
three black-box and open models on three datasets, featuring open-book,
closed-book, and commonsense QA. Further analysis reveals that multilingual
feedback is both an effective and a more equitable abstain strategy to serve
diverse language speakers, and cultural factors have great impact on language
selection and LLM abstention behavior, highlighting future directions for
multilingual and multi-cultural reliable language modeling.

多语言语言模型（LLMs）中的知识差距导致幻觉，通过在多语言环境中教授 LLMs 不认识的问题的方法可以减轻幻觉效应。本研究提出通过多语言反馈来增强 LLMs 的放弃行为，该方法通过在相关语言中生成多个反馈项，帮助识别不同语言、文化和社群之间的知识差距，并在三个数据集上的黑盒模型、开放书籍模型和常识问答模型中实验表明，这种多语言反馈方法优于其他强有力的基准方法，为低资源语言提供了 9.2% 的性能提升。进一步的分析显示，多语言反馈是一种有效且更公平的放弃策略，可以服务于不同的语言使用者，而文化因素对于语言选择和 LLM 放弃行为有很大的影响，为多语言和多文化可靠性语言建模提供了未来的发展方向。