| Challenge: | a recent study evaluated the psychological safety of large language models. |
| Approach: | They designed unbiased prompts to evaluate the psychological safety of large language models. |
| Outcome: | The proposed prompts showed that they were fine-tuned with behavioral metrics to reduce toxicity. |
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| Challenge: | Recent research has focused on examining Large Language Models’ characteristics from a psychological standpoint, acknowledging the necessity of understanding their behavioral characteristics. |
| Approach: | They propose to examine the reliability of personality tests to LLMs by using psychological scales. |
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A Chinese Dataset for Evaluating the Safeguards in Large Language Models (2024.findings-acl)
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Yuxia Wang, Zenan Zhai, Haonan Li, Xudong Han, Shom Lin, Zhenxuan Zhang, Angela Zhao, Preslav Nakov, Timothy Baldwin
| Challenge: | a recent study has shown that large language models can produce harmful responses, exposing users to unexpected risks. |
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| Challenge: | Recent studies have shown that LLMs can generate content that aligns with their assigned personality traits, but there is limited research on whether they consistently reflect specific personality traits. |
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Realistic Evaluation of Toxicity in Large Language Models (2024.findings-acl)
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| Challenge: | a large amount of data exposes large language models to toxicity and bias . prompt engineering can be easily bypassed with minimal prompt engineering. |
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SafeConf: A Confidence-Calibrated Safety Self-Evaluation Method for Large Language Models (2025.findings-emnlp)
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Bo Zhang, Cong Gao, Linkang Yang, Bingxu Han, Minghao Hu, Zhunchen Luo, Guotong Geng, Xiaoying Bai, Jun Zhang, Wen Yao, Zhong Wang
| Challenge: | Large language models (LLMs) have many advantages but they also pose significant safety risks. |
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Safety of Large Language Models Beyond English: A Systematic Literature Review of Risks, Biases, and Safeguards (2026.eacl-long)
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| Challenge: | Large language models (LLMs) have a growing number of applications that generate harmful, biased, or unsafe content. |
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The Art of Defending: A Systematic Evaluation and Analysis of LLM Defense Strategies on Safety and Over-Defensiveness (2024.findings-acl)
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| Challenge: | Recent work on Large Language Models (LLMs) has identified a number of approaches to protect against their vulnerabilities and safety. |
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Yingshui Tan, Boren Zheng, Baihui Zheng, Kerui Cao, Huiyun Jing, Jincheng Wei, Jiaheng Liu, Yancheng He, Wenbo Su, Xiaoyong Zhu, Bo Zheng, Kaifu Zhang
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Systematic Evaluation of Auto-Encoding and Large Language Model Representations for Capturing Author States and Traits (2025.findings-acl)
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Khushboo Singh, Vasudha Varadarajan, Adithya V Ganesan, August Håkan Nilsson, Nikita Soni, Syeda Mahwish, Pranav Chitale, Ryan L. Boyd, Lyle Ungar, Richard N Rosenthal, H. Schwartz
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LongSafety: Evaluating Long-Context Safety of Large Language Models (2025.acl-long)
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Yida Lu, Jiale Cheng, Zhexin Zhang, Shiyao Cui, Cunxiang Wang, Xiaotao Gu, Yuxiao Dong, Jie Tang, Hongning Wang, Minlie Huang
| Challenge: | Large Language Models (LLMs) have demonstrated remarkable capabilities in understanding and generating long sequences. |
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