@inproceedings{sun-etal-2025-causalabstain,
    title = "{C}ausal{A}bstain: Enhancing Multilingual {LLM}s with Causal Reasoning for Trustworthy Abstention",
    author = "Sun, Yuxi  and
      Zuo, Aoqi  and
      Gao, Wei  and
      Ma, Jing",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/corrections-2025-08/2025.findings-acl.723/",
    doi = "10.18653/v1/2025.findings-acl.723",
    pages = "14060--14076",
    ISBN = "979-8-89176-256-5",
    abstract = "Large Language Models (LLMs) often exhibit knowledge disparities across languages. Encouraging LLMs to $\textit{abstain}$ when faced with knowledge gaps is a promising strategy to reduce hallucinations in multilingual settings. Current abstention strategies for multilingual scenarios primarily rely on generating feedback in various languages using LLMs and performing self-reflection. However, these methods can be adversely impacted by inaccuracies and biases in the generated feedback. To address this, from a causal perspective, we introduce $\textit{CausalAbstain}$, a method that helps LLMs determine whether to utilize multiple generated feedback responses and how to identify the most useful ones. Extensive experiments demonstrate that $\textit{CausalAbstain}$ effectively selects helpful feedback and enhances abstention decisions with interpretability in both native language ($\textit{Casual-native}$) and multilingual ($\textit{Causal-multi}$) settings, outperforming strong baselines on two benchmark datasets covering encyclopedic and commonsense knowledge QA tasks."
}