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With the rapid advancement of large models (LMs), the development of general-purpose intelligent agents powered by LMs has become a reality.
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I. Shumailov, Y. Zhao, D. Bates, N. Papernot, R. Mullins, and R. Anderson, “Sponge examples: Energy-latency attacks on neural networks,” in Proc. EuroS&P , 2021, pp. 212–231
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F. Mireshghallah, K. Goyal, A. Uniyal, T. Berg-Kirkpatrick, and R. Shokri, “Quantifying privacy risks of masked language models using membership inference attacks,” in Proc. EMNLP , 2022, pp. 8332–8347
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N. Kandpal, E. Wallace, and C. Raffel, “Deduplicating training data mitigates privacy risks in language models,” in Proc. ICML , 2022, pp. 10 697–10 707
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N. Lee, W. Ping, P. Xu, M. Patwary, P. Fung, M. Shoeybi, and B. Catanzaro, “Factuality enhanced language models for open-ended text generation,” in Proc. NeurIPS , 2022, pp. 1–24
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C. Rebuffel, M. Roberti, L. Soulier, G. Scoutheeten, R. Cancelliere, and P. Gallinari, “Controlling hallucinations at word level in data-to-text generation,” Data Mining and Knowledge Discovery , pp. 1–37, 2022
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C. H. Song, J. Wu, C. Washington, B. M. Sadler, W.-L. Chao, and Y. Su, “LLM-planner: Few-shot grounded planning for embodied agents with large language models,” in Proc. IEEE/CVF ICCV , 2023, pp. 2998–3009
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X. Ma, G. Fang, and X. Wang, “LLM-pruner: On the structural pruning of large language models,” in Proc. NeurIPS , vol. 36, 2023, pp. 21 702–21 720
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T. Y. Zhuo, Z. Li, Y. Huang, F. Shiri, W. Wang, G. Haffari, and Y. Li, “On robustness of prompt-based semantic parsing with large pre-trained language model: An empirical study on codex,” in Proc. EACL , 2023, pp. 1090–1102
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N. Carlini, D. Ippolito, M. Jagielski, K. Lee, F. Tramèr, and C. Zhang, “Quantifying memorization across neural language models,” in Proc. ICLR , 2023, pp. 1–19
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S. Yao, J. Zhao, D. Yu, N. Du, I. Shafran, K. Narasimhan, and Y. Cao, “ReAct: Synergizing reasoning and acting in language models,” in Proc. ICLR , 2023, pp. 1–33
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N. Shinn, F. Cassano, E. Berman, A. Gopinath, K. Narasimhan, and S. Yao, “Reflexion: Language agents with verbal reinforcement learning,” in Proc. NeurIPS , 2023, pp. 8634–8652
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Z. Hu, A. Iscen, C. Sun, K.-W. Chang, Y. Sun, D. A. Ross, C. Schmid, and A. Fathi, “AVIS: Autonomous visual information seeking with large language model agent,” in Proc. NeurIPS , 2023, pp. 867–878
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Z. Wang, S. Cai, G. Chen, A. Liu, X. Ma, and Y. Liang, “Describe, explain, plan and select: Interactive planning with LLMs enables open-world multi-task agents,” in Proc. NeurIPS , 2023, pp. 1–37
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2023
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S. Gross and B. Krenn, The Role of Multimodal Data for Modeling Communication in Artificial Social Agents , 2023, pp. 83–93
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J. Ji, J. Wang, C. Huang, J. Wu, B. Xu, Z. Wu, J. Zhang, and Y. Zheng, “Spatio-temporal self-supervised learning for traffic flow prediction,” in Proc. AAAI , 2023, pp. 4356–4364
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D. Jiang, X. Ren, and B. Y. Lin, “LLM-blender: Ensembling large language models with pairwise ranking and generative fusion,” in Proc. ACL , 2023, pp. 14 165–14 178
2023
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S. Guo, Y. Wang, N. Zhang, Z. Su, T. H. Luan, Z. Tian, and X. Shen, “A survey on semantic communication networks: Architecture, security, and privacy,” IEEE Communications Surveys & Tutorials , pp. 1–34, 2024, doi:10.1109/COMST.2024.3516819
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2023
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F. Shi, X. Chen, K. Misra, N. Scales, D. Dohan, E. H. Chi, N. Schärli, and D. Zhou, “Large language models can be easily distracted by irrelevant context,” in Proc. ICML , vol. 202, 2023, pp. 31 210–31 227
2023
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C. Du, Y. Li, Z. Qiu, and C. Xu, “Stable diffusion is unstable,” in Proc. NeurIPS , 2023, pp. 1–22
2023
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C. Liang, X. Wu, Y. Hua, J. Zhang, Y. Xue, T. Song, Z. Xue, R. Ma, and H. Guan, “Adversarial example does good: Preventing painting imitation from diffusion models via adversarial examples,” in Proc. ICML , vol. 202, 2023, pp. 20 763–20 786
2023
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K. Greshake, S. Abdelnabi, S. Mishra, C. Endres, T. Holz, and M. Fritz, “Not what you’ve signed up for: Compromising real-world LLM-integrated applications with indirect prompt injection,” in Proc. AIsec , 2023, pp. 79–90
2023
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D. Bespalov, S. Bhabesh, Y. Xiang, L. Zhou, and Y. Qi, “Towards building a robust toxicity predictor,” in Proc. ACL , 2023, pp. 581–598
2023
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2023
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E. Jones, A. Dragan, A. Raghunathan, and J. Steinhardt, “Automatically auditing large language models via discrete optimization,” in Proc. ICML , 2023, pp. 15 307–15 329
2023
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2024
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G. Chen, S. Dong, Y. Shu, G. Zhang, J. Sesay, B. Karlsson, J. Fu, and Y. Shi, “Autoagents: A framework for automatic agent generation,” in Proc. IJCAI , 8 2024, pp. 22–30
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G. Qu, Z. Lin, F. Liu, X. Chen, and K. Huang, “TrimCaching: Parameter-sharing AI model caching in wireless edge networks,” in Proc. ICDCS , 2024, pp. 36–46
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R. Kong, Y. Li, Q. Feng, W. Wang, X. Ye, Y. Ouyang, L. Kong, and Y. Liu, “SwapMoE: Serving off-the-shelf MoE-based large language models with tunable memory budget,” in Proc. ACL , L.-W. Ku, A. Martins, and V. Srikumar, Eds., Aug. 2024, pp. 6710–6720
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C.-M. Chan, W. Chen, Y. Su, J. Yu, W. Xue, S. Zhang, J. Fu, and Z. Liu, “ChatEval: Towards better LLM-based evaluators through multi-agent debate,” in Proc. ICLR , 2024, pp. 1–15
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Z. Liu, Y. Zhang, P. Li, Y. Liu, and D. Yang, “A dynamic LLM-powered agent network for task-oriented agent collaboration,” in Proc. Conference on Language Modeling (COLM) , 2024, pp. 1–30
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Y. Yan and T. Hayakawa, “Hierarchical noncooperative dynamical systems under intragroup and intergroup incentives,” IEEE Transactions on Control of Network Systems , vol. 11, no. 2, pp. 743–755, 2024
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M. Zhong, C. An, W. Chen, J. Han, and P. He, “Seeking neural nuggets: Knowledge transfer in large language models from a parametric perspective,” in Proc. ICLR , 2024, pp. 1–21
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J. Han, N. Collier, W. Buntine, and E. Shareghi, “PiVe: Prompting with iterative verification improving graph-based generative capability of LLMs,” in Findings of the Association for Computational Linguistics ACL 2024 , 2024, pp. 6702–6718
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X. Xu, K. Kong, N. Liu, L. Cui, D. Wang, J. Zhang, and M. Kankanhalli, “An LLM can fool itself: A prompt-based adversarial attack,” in Proc. ICLR , 2024, pp. 1–23
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R. Liu, R. Yang, C. Jia, G. Zhang, D. Yang, and S. Vosoughi, “Training socially aligned language models on simulated social interactions,” in Proc. ICLR , 2024, pp. 1–24
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H. Wang, K. Dong, Z. Zhu, H. Qin, A. Liu, X. Fang, J. Wang, and X. Liu, “Transferable multimodal attack on vision-language pre-training models,” in Proc. SP , 2024, pp. 102–102
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H. Luo, J. Gu, F. Liu, and P. Torr, “An image is worth 1000 lies: Transferability of adversarial images across prompts on vision-language models,” in Proc. ICLR , 2024, pp. 1–22
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Z. Yu, X. Liu, S. Liang, Z. Cameron, C. Xiao, and N. Zhang, “Don’t listen to me: Understanding and exploring jailbreak prompts of large language models,” in Proc. USENIX , 2024, pp. 1–18
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Y. Yang, B. Hui, H. Yuan, N. Gong, and Y. Cao, “Sneakyprompt: Jailbreaking text-to-image generative models,” in Proc. SP , 2024, pp. 123–123
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X. Shen, Z. Chen, M. Backes, Y. Shen, and Y. Zhang, “”do anything now”: Characterizing and evaluating in-the-wild jailbreak prompts on large language models,” in Proc. CCS , 2024, pp. 1–22
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G. Deng, Y. Liu, Y. Li, K. Wang, Y. Zhang, Z. Li, H. Wang, T. Zhang, and Y. Liu, “Jailbreaker: Automated jailbreak across multiple large language model chatbots,” in Proc. NDSS , 2024, pp. 1–15
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S. Toyer, O. Watkins, E. A. Mendes, J. Svegliato, L. Bailey, T. Wang, I. Ong, K. Elmaaroufi, P. Abbeel, T. Darrell, A. Ritter, and S. Russell, “Tensor trust: Interpretable prompt injection attacks from an online game,” in Proc. ICLR , 2024, pp. 1–34
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L.-b. Ning, S. Wang, W. Fan, Q. Li, X. Xu, H. Chen, and F. Huang, “CheatAgent: Attacking LLM-empowered recommender systems via LLM agent,” in Proc. ACM KDD , 2024, p. 2284–2295
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M. Phute, A. Helbling, M. Hull, S. Peng, S. Szyller, C. Cornelius, and D. H. Chau, “LLM self defense: By self examination, LLMs know they are being tricked,” in Proc. ICLR , 2024, pp. 1–11
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Y. Zeng, Y. Wu, X. Zhang, H. Wang, and Q. Wu, “AutoDefense: Multi-agent LLM defense against jailbreak attacks,” in NeurIPS Safe Generative AI Workshop 2024 , 2024, pp. 1–25
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L. Shen, Y. Pu, S. Ji, C. Li, X. Zhang, C. Ge, and T. Wang, “Improving the robustness of transformer-based large language models with dynamic attention,” in Proc. NDSS , 2024, pp. 1–18
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S. Zhou, F. F. Xu, H. Zhu, X. Zhou, R. Lo, A. Sridhar, X. Cheng, T. Ou, Y. Bisk, D. Fried, U. Alon, and G. Neubig, “WebArena: A realistic web environment for building autonomous agents,” in Proc. ICLR , 2024, pp. 1–15
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S. Zhao, L. Gan, L. A. Tuan, J. Fu, L. Lyu, M. Jia, and J. Wen, “Defending against weight-poisoning backdoor attacks for parameter-efficient fine-tuning,” in Proc. NAACL Findings , 2024, pp. 3421–3438
2024
Closest in time.
C. Wei, W. Meng, Z. Zhang, M. Chen, M. Zhao, W. Fang, L. Wang, Z. Zhang, and W. Chen, “LMSanitator: Defending prompt-tuning against task-agnostic backdoors,” in Proc. NDSS , 2024, pp. 1–18
2024
Closest in time.
J. Xu, M. D. Ma, F. Wang, C. Xiao, and M. Chen, “Instructions as backdoors: Backdoor vulnerabilities of instruction tuning for large language models,” in Proc. NAACL , 2024, pp. 3111–3126
2024
Closest in time.
Z. Xiang, F. Jiang, Z. Xiong, B. Ramasubramanian, R. Poovendran, and B. Li, “BadChain: Backdoor chain-of-thought prompting for large language models,” in Proc. ICLR , 2024, pp. 1–28
2024
Closest in time.
A. Panda, C. A. Choquette-Choo, Z. Zhang, Y. Yang, and P. Mittal, “Teach LLMs to phish: Stealing private information from language models,” in Proc. ICLR , 2024, pp. 1–25
2024
Closest in time.
R. Staab, M. Vero, M. Balunovic, and M. Vechev, “Beyond memorization: Violating privacy via inference with large language models,” in Proc. ICLR , 2024, pp. 1–47
2024
Closest in time.
F. Kong, J. Duan, R. Ma, H. T. Shen, X. Shi, X. Zhu, and K. Xu, “An efficient membership inference attack for the diffusion model by proximal initialization,” in Proc. ICLR , 2024, pp. 1–19
2024
Closest in time.
W. Fu, H. Wang, C. Gao, G. Liu, Y. Li, and T. Jiang, “Membership inference attacks against fine-tuned large language models via self-prompt calibration,” in Proc. NeurIPS , 2024, pp. 1–30
2024
Closest in time.
L. Wang, J. Wang, J. Wan, L. Long, Z. Yang, and Z. Qin, “Property existence inference against generative models,” in Proc. USENIX , 2024, pp. 1–18
2024
Closest in time.
Z. Li, C. Wang, P. Ma, C. Liu, S. Wang, D. Wu, C. Gao, and Y. Liu, “On extracting specialized code abilities from large language models: A feasibility study,” in Proc. ICSE , 2024, pp. 1–13
2024
Closest in time.
X. Shen, Y. Qu, M. Backes, and Y. Zhang, “Prompt stealing attacks against text-to-image generation models,” in Proc. USENIX , 2024, pp. 1–20
2024
Closest in time.
2024
Closest in time.
B. Hui, H. Yuan, N. Gong, P. Burlina, and Y. Cao, “PLeak: Prompt leaking attacks against large language model applications,” in Proc. CCS , 2024, pp. 3600–3614
2024
Closest in time.
Y. Lin, Z. Gao, H. Du, D. Niyato, J. Kang, Z. Xiong, and Z. Zheng, “Blockchain-based efficient and trustworthy AIGC services in metaverse,” IEEE Transactions on Services Computing , vol. 17, no. 5, pp. 2067–2079, 2024
2024
Closest in time.
C. Mauran, “Samsung bans ChatGPT, AI chatbots after data leak blunder,” https://mashable.com/article/samsung-chatgpt-leak-leads-to-employee-ban
2024
Closest in time.
E. Jones, H. Palangi, C. S. Ribeiro, V. Chandrasekaran, S. Mukherjee, A. Mitra, A. H. Awadallah, and E. Kamar, “Teaching language models to hallucinate less with synthetic tasks,” in Proc. ICLR , 2024, pp. 1–18
2024
Closest in time.
M. Zhang, O. Press, W. Merrill, A. Liu, and N. A. Smith, “How language model hallucinations can snowball,” in Proc. ICML , 2024, pp. 1–13
2024
Closest in time.
N. Mündler, J. He, S. Jenko, and M. Vechev, “Self-contradictory hallucinations of large language models: Evaluation, detection and mitigation,” in Proc. ICLR , 2024, pp. 1–30
2024
Closest in time.
C. Chen, K. Liu, Z. Chen, Y. Gu, Y. Wu, M. Tao, Z. Fu, and J. Ye, “INSIDE: LLMs’ internal states retain the power of hallucination detection,” in Proc. ICLR , 2024, pp. 1–21
2024
Closest in time.
Y. Zhou, C. Cui, J. Yoon, L. Zhang, Z. Deng, C. Finn, M. Bansal, and H. Yao, “Analyzing and mitigating object hallucination in large vision-language models,” in Proc. ICLR , 2024, pp. 1–25
2024
Closest in time.
Z. Xi, W. Chen, X. Guo, W. He, Y. Ding, B. Hong, M. Zhang, J. Wang, S. Jin, E. Zhou, R. Zheng, X. Fan, X. Wang, L. Xiong, Y. Zhou, W. Wang, C. Jiang, Y. Zou, X. Liu, Z. Yin, S. Dou, R. Weng, W. Cheng, Q. Zhang, W. Qin, Y. Zheng, X. Qiu, X. Huang, and T. Gui, “The rise and potential of large language model based agents: A survey,” Science China Information Sciences , vol. 68, no. 2, pp. 1–44, 2025
2025
Closest in time.
D. Paglieri, B. Cupiał, S. Coward, U. Piterbarg, M. Wolczyk, A. Khan, E. Pignatelli, Ł. Kuciński, L. Pinto, R. Fergus, J. N. Foerster, J. Parker-Holder, and T. Rocktäschel, “BALROG: Benchmarking agentic LLM and VLM reasoning on games,” in Proc. ICLR , 2025, pp. 1–37
2025
Closest in time.
S. Marro, E. La Malfa, J. Wright, G. Li, N. Shadbolt, M. Wooldridge, and P. Torr, “Agora protocol: Scalable and reliable communication between your agents,” https://agoraprotocol.org/
2025
Closest in time.
L. Huang, W. Yu, W. Ma, W. Zhong, Z. Feng, H. Wang, Q. Chen, W. Peng, X. Feng, B. Qin, and T. Liu, “A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions,” ACM Transactions on Information Systems , vol. 43, 2025
2025
Closest in time.
G. Qu, Q. Chen, W. Wei, Z. Lin, X. Chen, and K. Huang, “Mobile edge intelligence for large language models: A contemporary survey,” IEEE Communications Surveys & Tutorials , pp. 1–42, 2025, doi:10.1109/COMST.2025.3527641
2025
Closest in time.
B. C. Das, M. H. Amini, and Y. Wu, “Security and privacy challenges of large language models: A survey,” ACM Computing Surveys , vol. 57, no. 6, 2025
2025
Closest in time.
“Figure 02 humanoid robot,” https://spectrum.ieee.org/figure-new-humanoid-robot
2025
Closest in time.
“Unitree G1,” https://www.unitree.com/g1
2025
Closest in time.
“Baidu apollo launches apollo ADFM large model,” https://autonews.gasgoo.com/icv/70033042.html
2025
Closest in time.
2025
Closest in time.
“AgenticFlow AI,” https://agenticflow.ai/
2025
Closest in time.
“Dify.AI,” https://github.com/langgenius/dify/
2025
Closest in time.
“RAGFlow,” https://github.com/infiniflow/ragflow
2025
Closest in time.
Y. Huang, H. Du, X. Zhang, D. Niyato, J. Kang, Z. Xiong, S. Wang, and T. Huang, “Large language models for networking: Applications, enabling techniques, and challenges,” IEEE Network , vol. 39, no. 1, pp. 235–242, 2025
2025
Closest in time.
Anthropic, “Model context protocol (MCP),” https://www.anthropic.com/news/model-context-protocol
2025
Closest in time.
“Agent network protocol (ANP),” https://agentnetworkprotocol.com/en/
2025
Closest in time.
R. Yi, L. Guo, S. Wei, A. Zhou, S. Wang, and M. Xu, “EdgeMoE: Empowering sparse large language models on mobile devices,” IEEE Transactions on Mobile Computing , no. 99, pp. 1–16, 2025, doi: 10.1109/TMC.2025.3546466
2025
Closest in time.
C. Qian, Z. Xie, Y. Wang, W. Liu, K. Zhu, H. Xia, Y. Dang, Z. Du, W. Chen, C. Yang, Z. Liu, and M. Sun, “Scaling large language model-based multi-agent collaboration,” in Proc. ICLR , 2025, pp. 1–18
2025
Closest in time.
H. Zhang, C. Zhu, X. Wang, Z. Zhou, C. Yin, M. Li, L. Xue, Y. Wang, S. Hu, A. Liu, P. Guo, and L. Y. Zhang, “BadRobot: Jailbreaking embodied LLMs in the physical world,” in Proc. ICLR , 2025, pp. 1–40
2025
Closest in time.
H. Zhang, J. Huang, K. Mei, Y. Yao, Z. Wang, C. Zhan, H. Wang, and Y. Zhang, “Agent security bench (ASB): Formalizing and benchmarking attacks and defenses in LLM-based agents,” in Proc. ICLR , 2025, pp. 1–36
2025
Closest in time.
J. Huang, H. Shao, and K. C.-C. Chang, “Are large pre-trained language models leaking your personal information?” in Proc. EMNLP, Findings , 2022, pp. 2038–2047
2047
Closest in time.