Papers by Yahan Yang
MrGuard: A Multilingual Reasoning Guardrail for Universal LLM Safety (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) are susceptible to adversarial attacks such as jailbreaking, which can elicit harmful or unsafe behaviors. |
| Approach: | They propose a multilingual guardrail with reasoning for prompt classification that integrates culturally and linguistically nuanced variants and supervised fine-tuning. |
| Outcome: | The proposed guardrail outperforms baselines across in-domain and out-of-domain languages by more than 15%. |
When Large Language Models Meet Speech: A Survey on Integration Approaches (2025.findings-acl)
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| Challenge: | Recent advances in large language models have spurred interest in expanding their application beyond text-based tasks. |
| Approach: | They propose to categorize the integration of speech with LLMs into three main approaches . they demonstrate how these methods are applied across various speech-related applications . |
| Outcome: | The proposed methods are applied across speech-related applications and highlight the challenges in this field to offer inspiration for future research. |
SpeechIQ: Speech-Agentic Intelligence Quotient Across Cognitive Levels in Voice Understanding by Large Language Models (2025.acl-long)
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Zhen Wan, Chao-Han Huck Yang, Yahan Yu, Jinchuan Tian, Sheng Li, Ke Hu, Zhehuai Chen, Shinji Watanabe, Fei Cheng, Chenhui Chu, Sadao Kurohashi
| Challenge: | SIQ quantifies voice understanding abilities and provides unified comparisons between cascaded methods and end-to-end models. |
| Approach: | They propose a human cognition-inspired evaluation pipeline for voice understanding large language models (LLM_Voice) that quantifies voice understanding abilities and provides unified comparisons between cascaded methods and end-to-end models. |
| Outcome: | The proposed framework quantifies voice understanding abilities and provides unified comparisons between cascaded methods and end-to-end models, identifies annotation errors in existing benchmarks, and detects hallucinations in LLM_Voice. |
In and Out-of-Domain Text Adversarial Robustness via Label Smoothing (2023.acl-short)
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| Challenge: | Existing studies show that state-of-the-art NLP models are vulnerable to adversarial attacks . label smoothing has been proven effective in a variety of applications and modalities . |
| Approach: | They propose to use label smoothing to improve adversarial robustness in pre-trained models against various popular attacks. |
| Outcome: | The proposed method significantly improves adversarial robustness in pre-trained models against various popular attacks. |
Bootstrapping Small & High Performance Language Models with Unmasking-Removal Training Policy (2023.emnlp-main)
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| Challenge: | Large-scale pre-trained language models (LMs) have shown promising ability on handling various downstream tasks including textual classification and question answering. |
| Approach: | They propose to use BabyBERTa to train child-directed speech without unmasking words while masking parameters to improve grammatical accuracy. |
| Outcome: | The proposed model achieves grammatical ability comparable to RoBERTa-base model, which is trained on 6,000 times more words and 15 times more parameters. |