Computer Science ›› 2026, Vol. 53 ›› Issue (9): 16-23.doi: 10.11896/jsjkx.260100147
• Research and Application of Large Language Model Technology • Previous Articles Next Articles
LI Luozheng1, LI Lingbo1, YUAN Quan2
CLC Number:
| [1] BROWN T B,MANN B,RYDER N,et al.Language Models are Few-Shot Learners[J].Advances in Neural Information Processing Systems,2020,33:1877-1901. [2] BOMMASANI R,HUDSON D A,ADELI E,et al.On the Opportunities and Risks of Foundation Models[J].arXiv:2108.07258,2021. [3] ZENG A,LIU X,DU Z,et al.GLM-130B:an open bilingual pre-trained model[J].arXiv:2210.02414,2022. [4] BAO H,ZHOU P,WANG X,et al.Ethical and Social Risks of Large Language Models:A Survey[J].Journal of Artificial Intelligence Research,2024,79:1-55. [5] WEI A,HAGHTALAB N,STEINHARDT J.Jailbro-ken:How Does LLM Safety Training Fail?[C] //Proceedings of the 40th International Conference on Machine Learning.2023:34567-34583. [6] SHEN L,LI R,LIU Y,et al.Chain-of-Thought Hijack-ing:A NewJailbreaking Attack on Large Language Models[J].arXiv:2501.01289,2025. [7] LI X T,WU J,ZHENG Q H,et al.Jailbreak attacks on large language models:models,root causes,and the evolution of attacks and defenses [J].Science China Information Sciences,2025,55:1372-1405. [8] WANG Y,CHEN Z,LIU J,et al.Sugar-Coated Poison:Bypas-sing LLM Safety Filters through Benign-Content Prefixes[C] //Proceedings of the 2025 ACM SIGSAC Conference on Computer and Communications Security.2025:1-15. [9] RÖTTGER P,PERNISI F,VIDGEN B,et al.Safety prompts:a systematic review of open datasets for evaluating and improving large language model safety[C] //Proceedings of the 2025 AAAI Conference on Artificial Intelligence.2025:27617-27627. [10] OUYANG L,WU J,JIANG X,et al.Training Language Models to Follow Instructions with Human Feedback[J].Advances in Neural Information Processing Systems,2022,35:27730-27744. [11] WANG K,ZHANG G B,ZHOU Z H,et al.A ComprehensiveSurvey in LLM(-Agent) Full Stack Safety:Data,Training and Deployment[J].arXiv:2504.15585,2025. [12] CHUA J,LI Y,YANG S,et al.AI Safety in Generative AI Large Language Models:A Survey[J].arXiv:2407.18369,2024. [13] ZHANG Z,LU Y,MA J,et al.ShieldLM:Empower-ing LLMs as Aligned,Customizable and Explainable Safety Detectors[C] //Findings of the Association for Computational Linguistics(EMNLP 2024).ACL,2024:10420-10438. [14] LI Z J.Research on safety alignment techniques for large language models [D].Harbin:Harbin Institute of Technology,2025. [15] DENG G,LIU Y,LI Y,et al.Masterkey:Automated Jailbrea-king of Large Language Model Chat-bots[C] //Proceedings 2024 Network and Distributed System Security Symposium.2024. [16] HARTVIGSEN T,GABRIEL S,PALANGI H,et al.ToxiGen:A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection[C] //Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics.2022:3309-3326. [17] ANTYPAS D,CAMACHO-COLLADOS J.Robust Hate Speech Detection in Social Media:A Cross-Dataset Empirical Evaluation[C] //The 7th Workshop on Online Abuse and Harms(WOAH).ACL,2023:231-242. [18] ALMOHAIMEED S,ALMOHAIMEED S,SHAFIN A A,et al.THOS:A Benchmark Dataset for Targeted Hate and Offensive Speech[C] //Proceedings of Datacentric Machine Learning Research(DMLR) Workshop at ICML 2023.2023. [19] INAN H,UPASANI K,CHI J,et al.Llama Guard:LLM-based Input-Output Safeguard for Human-AI Conversations[J].ar-Xiv:2312.06674,2023. [20] ZHAO H,YUAN C,HUANG F,et al.Qwen3Guard Technical Report[J].arXiv:2510.14276,2025. [21] HU E J,SHEN Y,WALLIS P,et al.LoRA:Low-Rank Adaptation of Large Language Models[C] //ICLR.2022:3. [22] YI X Y,XIE X.An analysis of moral and value alignment issues in large models [J].Journal of Computer Research and Development,2023,60(9):1926-1945. [23] BAI Y,KADAVATH S,KUNDU S,et al.Constitutional AI:Harmlessness from AI Feedback[J].arXiv:2212.08073,2022. [24] HUANG Y,ZHANG H,WANG L,et al.Lazy Safety Align-ment:Efficient Safety Fine-tuning for Large Language Models[C] //Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics.2024:456-470. [25] CAO Z,YANG Y,ZHAO H.SCANS:Mitigating the Exaggerated Safety for LLMs via Safety-Conscious Activation Steering[C] //Proceedings of the AAAI Conference on Artificial Intelligence.2025:23523-23531. [26] XIE Y,YI J,SHAO J,et al.Defending ChatGPT Against Jailbreak Attack via Self-Reminders[J].Nature Machine Intelligence,2023,5(12):1486-1496. [27] CHAO P,ROBEY A,DOBRIBAN E,et al.Jailbreaking Black Box Large Language Models in Twenty Queries[C] //R0-FoMo:Robustness of Few-shot and Zero-shot Learning in Large Foundation Models.2023. [28] ZHANG Z,YANG J,KE P,et al.Defending Large LanguageModels Against Jailbreaking Attacks Through Goal Prioritization[C] //Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics.ACL,2024:8865-8887. [29] WU X,WANG R,YANG Y,et al.Improving LLM Safety Alignment with Dual-Objective Optimiza-tion[C] //Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing.2024:5678-5693. [30] DU T,WEI Z,CHEN Q,et al.Advancing LLM Safe Alignment with Safety Representation Ranking[C] //ICML 2025 Workshop on Reliable and Responsible Foundation Models.2025. [31] SUN H,ZHANG Z,DENG J,et al.Safety Assessment of Chinese Large Language Models[J].arXiv:2304.10436,2023. [32] YUAN X,LI J,WANG D,et al.S-Eval:Towards Automatedand Comprehensive Safety Evaluation for Large Language Mo-dels[C] //Proceedings of the ACM on Software Engineering.2025:2136-2157. [33] XU L,ZHAO K,ZHU L,et al.SC-Safety:A Multi-round Open-ended Question Adversarial Safety Benchmark for Large Language Models in Chinese[J].arXiv:2310.05818,2023. [34] WANG Y,ZHAI Z,LI H,et al.A Chinese Dataset for Evaluating the Safeguards in Large Language Models[C] //Findings of the Association for Computational Linguistics:ACL 2024.2024:3106-3119. [35] WANG Y,ZHAI Z,LI H,et al.A Chinese Dataset for Evaluating the Safeguards in Large Language Models[J].arXiv:2402.12193,2024. [36] TAN Y,ZHENG B,ZHENG B,et al.Chinese SafetyQA:ASafety Short-form Factuality Benchmark for Large Language Models[J].arXiv:2412.15265,2024. [37] ZHANG H,GAO H,HU Q,et al.ChineseSafe:A ChineseBenchmark for Evaluating Safety in Large Language Models[J].arXiv:2410.18491,2025. [38] ZHANG W,LEI X,LIU Z,et al.CHiSafetyBench:A Chinese Hierarchical Safety Benchmark for Large Language Models[J].arXiv:2406.10311,2024. [39] XU G,LIU J,YAN M,et al.CValues:Measuring the Values of Chinese Large Language Models from Safety to Responsibility[J].arXiv:2307.09705,2023. [40] ZHANG M,PAN X,YANG M.Jade:A Linguistics-Based Safety Evaluation Platform for LLM[J].arXiv:2311.00286,2023. [41] CHEN Z,YU H,WU X,et al.Libra:Large Chines Based Safeguard for AI Content[J].arXiv:2507.21929,2025. [42] ZHANG Z,LEI L,WU L,et al.SafetyBench:Evaluating theSafety of Large Language Models[J].arXiv:2309.07045,2023. [43] ROTTGER P,KIRK H,VIDGEN B,et al.XSTest:A Test Suitefor Identifying Exaggerated Safety Behaviours in Large Language Models[C] //Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies.ACL,2024:5377-5400. [44] SHI C,WANG X,GE Q,et al.Navigating the Over-Kill inLarge Language Models[C] //Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics.ACL,2024:4602-4614. [45] CUI J,CHIANG W L,STOICA I,et al.OR-Bench:An Over-Refusal Benchmark for Large Language Mod-els[J].arXiv:2405.20947,2024. [46] ZHANG Z,XU W,WU F,et al.FalseReject:A Resource for Improving Contextual Safety and Mitigating Over-Refusals in LLMs via Structured Reasoning[J].arXiv:2505.08054,2025. [47] WANG Z,TU H,WANG Y,et al.STAR-1:Safer Alignment of Reasoning LLMs with 1K Data[J].arXiv:2504.01903,2025. [48] HUANG T,HU S,ILHAN F,et al.Safety Tax:Safety Align-ment Makes Your Large Reasoning Models Less Reasonable[J].arXiv:2503.00555,2025. [49] QI X,PANDA A,LYU K,et al.Safety Alignment Should BeMade More Than Just a Few Tokens Deep[J].arXiv:2406.05946,2024. [50] CHEN J,WANG X,YAO Z,et al.Finding Safety Neurons in Large Language Models[J].arXiv:2406.14144,2024. [51] XUE Y,MIRZASOLEIMAN B.LoRA is All You Need for Safety Alignment of Reasoning LLMs[J].arXiv:2507.17075,2025. [52] JI J,WANG K,QIU T A,et al.Language Models Re-sist Alignment:Evidence from Data Compression[C] //Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics.2025:23411-23432. [53] KANG D,PARK J,JO Y,et al.From values to opinions:predicting human behaviors and stances using value-injected large language models[C] //Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing.ACL,2023:15539-15559. [54] YAO J,YI X,XIE X.CLAVE:an adaptive framework for evaluating values of LLM generated responses[J].Advances in Neural Information Processing Systems,2024,37:58868-58900. |
| [1] | HE Jiaojun, LI Xin. Review of Graph Learning Based on Large Language Models:Methods,Benchmarks and Advances [J]. Computer Science, 2026, 53(9): 1-15. |
| [2] | ZHANG Rongjie, PANG Xiongwen, WANG Fengling. Review of Large Language Model-based Time Series Modeling via Fine-tuning and AgentArchitecture [J]. Computer Science, 2026, 53(9): 55-70. |
| [3] | GUO Yuyang, SHI Lei, LIU Huan, DONG Yixiang, LI Rui. Domain-adapted and Dynamically Retrieval-augmented Approach for Large-scale History Discipline Model Construction [J]. Computer Science, 2026, 53(9): 92-100. |
| [4] | LI Zhennan, QIAN Jiayan, WANG Xinzhi, ZHANG Hui. Technology Risk Structure Recognition Based on Multi-granularity Semantic Dual Reflection [J]. Computer Science, 2026, 53(9): 395-404. |
| [5] | LIU Jing. Review of Music Artificial Intelligence Driven by Large Language Models [J]. Computer Science, 2026, 53(8): 229-244. |
| [6] | ZHANG Haoran, HAO Wenning, JIN Dawei, CHENG Kai, LIU Junyang. Agentic Retrieval Augmented Generation Framework Based on Retrieval Task Planning and Reflection Mechanism [J]. Computer Science, 2026, 53(8): 285-297. |
| [7] | CHEN Zhixiang, XIE Zhipeng. Event Causal Data Augmentation Method Based on Large Language Model [J]. Computer Science, 2026, 53(7): 125-131. |
| [8] | XU Rui, LIU Jin, LIU Xudong, GUAN Jian, DONG Wei. Exploring the Generalization Ability of Prompt-based Large Language Models for TextClassification [J]. Computer Science, 2026, 53(6A): 250400092-7. |
| [9] | WEI Qing, ZHANG Yupeng, LIU Shaoxun, ZHANG Jinfeng, ZHANG Yuezhong, CHEN Haoyang. Fuzzing Driver Generation Based on Large Language Models [J]. Computer Science, 2026, 53(6A): 250400113-8. |
| [10] | ZHANG Yongyu, GUO Chenjuan, FEI Xueqin, LI Feng. Study on Financial Text Sentiment Analysis Method Based on Large Language Models with Market Feedback Supervision [J]. Computer Science, 2026, 53(6A): 250500073-14. |
| [11] | SHI Hongxu, LIU Yi, LIU Kun. Survey of Recommendation Systems Based on Large Language Models [J]. Computer Science, 2026, 53(6): 281-303. |
| [12] | WANG Shenghui, LI Teng. Innovative Automated Scoring Based on Large Language Models [J]. Computer Science, 2026, 53(5): 90-98. |
| [13] | HU Junjie, CHEN Yujie, HU Yikun, WEN Cheng, CAO Jialun, MA Zhi, SU Jie, SUN Weidi, TIAN Cong, QIN Shengchao. Formal Theorem Proving Empowered by Large Language Model:Survey and Perspectives [J]. Computer Science, 2026, 53(4): 1-23. |
| [14] | LIU Suyi, LIU Qi, GAO Weibo. Agent4Stu:Efficient LLM-based Student Answer Behavior Simulation Agent [J]. Computer Science, 2026, 53(4): 347-355. |
| [15] | XU Cheng, LIU Yuxuan, WANG Xin, ZHANG Cheng, YAO Dengfeng, YUAN Jiazheng. Review of Speech Disorder Assessment Methods Driven by Large Language Models [J]. Computer Science, 2026, 53(3): 307-320. |
|
||