Cui Danxin — accepted AI-conference papers

2026

Measuring the Unmeasurable: Unveiling Latent Cognitive Capabilities of LLM

Cui Danxin, Sihang Jiang, Keyi Wang, Zhiyi Duan, Yanghua Xiao, Bi Yude +4

AAAI 2026technical

As large language models (LLMs) are increasingly deployed in high-stakes domains such as education, healthcare, and law, accurately evaluating their nuanced reasoning process becomes essential to ensure their safety, reliability, and trustworthiness. However, most existing benchmarks evaluate LLMs a

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2025

Data-Efficient Selection via Grammatical Complexity in Continual Pre-training of Domain-Specific LLMs

Yizhou Ying, Geng Zhang, Cui Danxin, Chengyu Du, Guanglei Yue, Sihang Jiang +4

EMNLP 2025

Data efficiency is crucial in domain-specific continual pre-training (CPT) of large language models (LLMs), especially under resource constraints. Aiming for “small data, big impact,” this work addresses the limitations of existing domain-specific data selection strategies, which often rely on scarc

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