CausalAbstain: Enhancing Multilingual LLMs with Causal Reasoning for Trustworthy Abstention

• URL: http://arxiv.org/abs/2506.00519v2
• Date: Tue, 03 Jun 2025 09:58:17 GMT
• Title: CausalAbstain: Enhancing Multilingual LLMs with Causal Reasoning for Trustworthy Abstention
• Authors: Yuxi Sun, Aoqi Zuo, Wei Gao, Jing Ma,
• Abstract summary: Large Language Models (LLMs) often exhibit knowledge disparities across languages.<n>We introduce textitCausalAbstain, a method that helps LLMs determine whether to utilize multiple generated feedback responses.<n>Experiments demonstrate that textitCausalAbstain effectively selects helpful feedback and enhances abstention decisions with interpretability.
• Score: 9.76878200328024
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 (\textsc{Casual-native}) and multilingual (\textsc{Causal-multi}) settings, outperforming strong baselines on two benchmark datasets covering encyclopedic and commonsense knowledge QA tasks. Our code and data are open-sourced at https://github.com/peachch/CausalAbstain.

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