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cs.CL2026
Lower-Resource, Higher Scores: Language Bias in LLM Evaluators
Ej Zhou, Lucas Resck, Zheng Hui +1
LLM evaluators (trained reward models and prompted LLM-as-a-Judge) are routinely validated via pairwise accuracy. In a multilingual setting, this operates under the premise that hi…
cs.CL2026
Mitigating Cross-Lingual Cultural Inconsistencies in LLMs via Consensus-Driven Preference Optimisation
Lucas Resck, Isabelle Augenstein, Anna Korhonen
Multilingual large language models (MLLMs) often answer culturally ambiguous questions inconsistently across languages, even for a fixed user persona -- e.g., Shakespeare in Englis…