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20242026
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cs.CL2026

Qwen-Scope: Turning Sparse Features into Development Tools for Large Language Models

Boyi Deng, Xu Wang, Yaoning Wang +15

Large language models have achieved remarkable capabilities across diverse tasks, yet their internal decision-making processes remain largely opaque, limiting our ability to inspec…

cs.CL2026

SASFT: Sparse Autoencoder-guided Supervised Finetuning to Mitigate Unexpected Code-Switching in LLMs

Boyi Deng, Yu Wan, Baosong Yang +3

Large Language Models (LLMs) have impressive multilingual capabilities, but they suffer from unexpected code-switching, also known as language mixing, which involves switching to u…

cs.CL2025

Qwen3Guard Technical Report

Haiquan Zhao, Chenhan Yuan, Fei Huang +40

As large language models (LLMs) become more capable and widely used, ensuring the safety of their outputs is increasingly critical. Existing guardrail models, though useful in stat…

cs.CL2025

CultureSynth: A Hierarchical Taxonomy-Guided and Retrieval-Augmented Framework for Cultural Question-Answer Synthesis

Xinyu Zhang, Pei Zhang, Shuang Luo +4

Cultural competence, defined as the ability to understand and adapt to multicultural contexts, is increasingly vital for large language models (LLMs) in global environments. While…

cs.CL2025

Unveiling Language-Specific Features in Large Language Models via Sparse Autoencoders

Boyi Deng, Yu Wan, Yidan Zhang +2

The mechanisms behind multilingual capabilities in Large Language Models (LLMs) have been examined using neuron-based or internal-activation-based methods. However, these methods o…

cs.CL2025

P-MMEval: A Parallel Multilingual Multitask Benchmark for Consistent Evaluation of LLMs

Yidan Zhang, Yu Wan, Boyi Deng +6

Recent advancements in large language models (LLMs) showcase varied multilingual capabilities across tasks like translation, code generation, and reasoning. Previous assessments of…