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cs.CL2026

PolyFact: Comparing Consistency-Driven Post-training Methods for Cross-Lingual Factual Recall

Jonathan von Rad, Louis Arts, George Burgess +6

Large language models (LLMs) trained predominantly on English data encode substantial world knowledge, yet often fail to express it reliably in other languages, a phenomenon known…

cs.CL2026

ShapleyLaw: A Game-Theoretic Approach to Multilingual Scaling Laws

Xuyang Cao, Qianying Liu, Chuan Xiao +7

In multilingual pretraining, the test loss of a pretrained model is heavily influenced by the proportion of each language in the pretraining data, namely the \textit{language mixtu…

cs.CL2026

The Role of Mixed-Language Documents for Multilingual Large Language Model Pretraining

Jiandong Shao, Raphael Tang, Crystina Zhang +4

Multilingual large language models achieve impressive cross-lingual performance despite largely monolingual pretraining. While bilingual data in pretraining corpora is widely belie…

cs.CL2025

SSA-COMET: Do LLMs Outperform Learned Metrics in Evaluating MT for Under-Resourced African Languages?

Senyu Li, Jiayi Wang, Felermino D. M. A. Ali +7

Evaluating machine translation (MT) quality for under-resourced African languages remains a significant challenge, as existing metrics often suffer from limited language coverage a…

cs.CL2025

Drawing Conclusions from Draws: Rethinking Preference Semantics in Arena-Style LLM Evaluation

Raphael Tang, Crystina Zhang, Wenyan Li +3

In arena-style evaluation of large language models (LLMs), two LLMs respond to a user query, and the user chooses the winning response or deems the "battle" a draw, resulting in an…

cs.CL2025

Translate, then Detect: Leveraging Machine Translation for Cross-Lingual Toxicity Classification

Samuel J. Bell, Eduardo Sánchez, David Dale +3

Multilingual toxicity detection remains a significant challenge due to the scarcity of training data and resources for many languages. While prior work has leveraged the translate-…