Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

8-2025

Abstract

Large Language Models (LLMs) often exhibit knowledge disparities across languages. Encouraging LLMs to 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 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 CausalAbstain effectively selects helpful feedback and enhances abstention decisions with interpretability in both native language (Casual-native) and multilingual (Causal-multi) settings, outperforming strong baselines on two benchmark datasets covering encyclopedic and commonsense knowledge QA tasks.

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Areas of Excellence

Digital transformation

Publication

Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), Vienna, Austria, July 27 - August 1

First Page

14060

Last Page

14076

Identifier

10.18653/v1/2025.findings-acl.723

City or Country

Vienna, Austria

Additional URL

https://aclanthology.org/2025.findings-acl.723/

Download

Share

COinS