5 papers
Language Confusion Gate: Language-Aware Decoding Through Model Self-Distillation
Collin Zhang, Fei Huang, Chenhan Yuan +1
Large language models (LLMs) often experience language confusion, which is the unintended mixing of languages during text generation. Current solutions to this problem either neces…
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…
CARE: Decoding Time Safety Alignment via Rollback and Introspection Intervention
Xiaomeng Hu, Fei Huang, Chenhan Yuan +2
As large language models (LLMs) are increasingly deployed in real-world applications, ensuring the safety of their outputs during decoding has become a critical challenge. However,…
CAST: Corpus-Aware Self-similarity Enhanced Topic modelling
Yanan Ma, Chenghao Xiao, Chenhan Yuan +4
Topic modelling is a pivotal unsupervised machine learning technique for extracting valuable insights from large document collections. Existing neural topic modelling methods often…
Predicting Rewards Alongside Tokens: Non-disruptive Parameter Insertion for Efficient Inference Intervention in Large Language Model
Chenhan Yuan, Fei Huang, Ru Peng +4
Transformer-based large language models (LLMs) exhibit limitations such as generating unsafe responses, unreliable reasoning, etc. Existing inference intervention approaches attemp…