Kavli Affiliate: Wei Gao
| First 5 Authors: Yuxi Sun, Aoqi Zuo, Wei Gao, Jing Ma,
| Summary:
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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