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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
DatedGPT: Preventing Lookahead Bias in Large Language Models with Time-Aware Pretraining
Yutong Yan, Raphael Tang, Zhenyu Gao +2
Large language models pretrained on internet-scale data risk lookahead bias in forecasting tasks, as they may have already seen the true outcome during training. To address this, w…
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…