---
title: "Who cites The WMDP Benchmark: — 114 citing papers"
description: "114 papers of the Elamur Research library cite «The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning», found in…"
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# Who cites «The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning»

114 papers of our library cite it (of the 149,439 whose bibliographies we read) · 29 with a page here, the most cited first · [← back to «The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning»](https://md.research.elamur.ai/free/en/papers/li-2024-wmdp-benchmark-measuring-reducing-malicious/)

1. [**Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities arXiv:2507.06261**Gheorghe Comanici, Eric Bieber, Mike Schaekermann, et al.*2025 · 974 citations*](https://md.research.elamur.ai/free/en/papers/comanici-2025-gemini-2-5-pushing-frontier/)
2. **[Humanity's Last Exam](https://md.research.elamur.ai/free/en/papers/phan-2025-humanity-s-last-exam/) [Nature](https://md.research.elamur.ai/free/en/venues/nature/)**Long Phan, Alice Gatti, Ziwen Han, et al.*2025 · 256 citations*
3. [**Olmo 3 arXiv:2512.13961**Team Olmo: Allyson Ettinger, Amanda Bertsch, Bailey Kuehl, et al.*2025 · 130 citations*](https://md.research.elamur.ai/free/en/papers/ettinger-2025-olmo-3/)
4. [**Negative Preference Optimization: From Catastrophic Collapse to Effective Unlearning arXiv:2404.05868**Ruiqi Zhang, Licong Lin, Yu Bai, et al.*2024 · 126 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2024-negative-preference-optimization-catastrophic-collapse/)
5. [**Foundational Challenges in Assuring Alignment and Safety of Large Language Models arXiv:2404.09932**Usman Anwar, Abulhair Saparov, Javier Rando, et al.*2024 · 87 citations*](https://md.research.elamur.ai/free/en/papers/anwar-2024-foundational-challenges-assuring-alignment-safety/)
6. **[Improving Alignment and Robustness with Circuit Breakers](https://md.research.elamur.ai/free/en/papers/zou-2024-improving-alignment-robustness-circuit-breakers/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Andy Zou, Long Phan, Justin Wang, et al.*2024 · 83 citations*
7. **[SOUL: Unlocking the Power of Second-Order Optimization for LLM Unlearning](https://md.research.elamur.ai/free/en/papers/jia-2024-soul-unlocking-power-second-order/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jinghan Jia, Yihua Zhang, Yimeng Zhang, et al.*2024 · 46 citations*
8. [**SafetyPrompts: a Systematic Review of Open Datasets for Evaluating and Improving Large Language Model Safety arXiv:2404.05399**Paul Rottger, Fabio Pernisi, Bertie Vidgen, et al.*2024 · 33 citations*](https://md.research.elamur.ai/free/en/papers/rottger-2024-safetyprompts-systematic-review-open-datasets/)
9. [**Constitutional Classifiers: Defending against Universal Jailbreaks across Thousands of Hours of Red Teaming arXiv:2501.18837**Mrinank Sharma, Meg Tong, Jesse Mu, et al.*2025 · 29 citations*](https://md.research.elamur.ai/free/en/papers/sharma-2025-constitutional-classifiers-defending-against-universal/)
10. **[Protecting Privacy in Multimodal Large Language Models with MLLMU-Bench](https://md.research.elamur.ai/free/en/papers/liu-2025-protecting-privacy-multimodal-large-language/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Zheyuan Liu, Guangyao Dou, Mengzhao Jia, et al.*2025 · 24 citations*
11. **[LLMs know their vulnerabilities: Uncover Safety Gaps through Natural Distribution Shifts](https://md.research.elamur.ai/free/en/papers/ren-2025-llms-know-their-vulnerabilities-uncover/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Qibing Ren, Hao Li, Dongrui Liu, et al.*2025 · 18 citations*
12. **[Soft Prompting for Unlearning in Large Language Models](https://md.research.elamur.ai/free/en/papers/bhaila-2025-soft-prompting-unlearning-large-language/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Karuna Bhaila, Minh-Hao Van, Xintao Wu*2025 · 16 citations*
13. **[Measuring Chain of Thought Faithfulness by Unlearning Reasoning Steps](https://md.research.elamur.ai/free/en/papers/tutek-2025-measuring-chain-thought-faithfulness-unlearning/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Martin Tutek, Fateme Hashemi Chaleshtori, Ana Marasovic, et al.*2025 · 15 citations*
14. **[Modality-Aware Neuron Pruning for Unlearning in Multimodal Large Language Models](https://md.research.elamur.ai/free/en/papers/liu-2025-modality-aware-neuron-pruning-unlearning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zheyuan Liu, Guangyao Dou, Xiangchi Yuan, et al.*2025 · 12 citations*
15. **[Alternate Preference Optimization for Unlearning Factual Knowledge in Large Language Models](https://md.research.elamur.ai/free/en/papers/mekala-2025-alternate-preference-optimization-unlearning-factual/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Anmol Mekala, Vineeth Dorna, Shreya Dubey, et al.*2025 · 10 citations*
16. **[Model Unlearning via Sparse Autoencoder Subspace Guided Projections](https://md.research.elamur.ai/free/en/papers/wang-2025-model-unlearning-sparse-autoencoder-subspace/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Xu Wang, Zihao Li, Benyou Wang, et al.*2025 · 8 citations*
17. **[Get Confused Cautiously: Textual Sequence Memorization Erasure with Selective Entropy Maximization](https://md.research.elamur.ai/free/en/papers/zhang-2025-get-confused-cautiously-textual-sequence/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Zhaohan Zhang, Ziquan Liu, Ioannis Patras*2025 · 7 citations*
18. **[LED-Merging: Mitigating Safety-Utility Conflicts in Model Merging with Location-Election-Disjoint](https://md.research.elamur.ai/free/en/papers/ma-2025-led-merging-mitigating-safety-utility/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Qianli Ma, Dongrui Liu, Qian Chen, et al.*2025 · 5 citations*
19. **[BLUR: A Bi-Level Optimization Approach for LLM Unlearning](https://md.research.elamur.ai/free/en/papers/reisizadeh-2026-blur-bi-level-optimization-approach/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Hadi Reisizadeh, Jinghan Jia, Zhiqi Bu, et al.*2026 · 4 citations*
20. **[CTRAP: Embedding Collapse Trap to Safeguard Large Language Models from Harmful Fine-Tuning](https://md.research.elamur.ai/free/en/papers/yi-2026-ctrap-embedding-collapse-trap-safeguard/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Biao Yi, Tiansheng Huang, Baolei Zhang, et al.*2026 · 3 citations*
21. **[Modeling LLM Unlearning as an Asymmetric Two-Task Learning Problem](https://md.research.elamur.ai/free/en/papers/xiao-2026-modeling-llm-unlearning-as-asymmetric/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zeguan Xiao, Siqing Li, Yong Wang, et al.*2026 · 2 citations*
22. **[Erasing Without Remembering: Implicit Knowledge Forgetting in Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2026-erasing-without-remembering-implicit-knowledge/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Huazheng Wang, Yongcheng Jing, Haifeng Sun, et al.*2026 · 1 citation*
23. **[Calibration vs Decision Making: Revisiting the Reliability Paradox in Unlearned Language Models](https://md.research.elamur.ai/free/en/papers/shukla-2026-calibration-vs-decision-making-revisiting/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Divyaksh Shukla, Ashutosh Modi*2026*
24. **[CAP: Controllable Alignment Prompting for Unlearning in LLMs](https://md.research.elamur.ai/free/en/papers/wang-2026-cap-controllable-alignment-prompting-unlearning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhaokun Wang, Jinyu Guo, Jingwen Pu, et al.*2026*
25. **[Decoding-Unlearning: Fact Forgetting via Entropy-Guided Inference](https://md.research.elamur.ai/free/en/papers/pu-2026-decoding-unlearning-fact-forgetting-entropy/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jingwen Pu, Mingjun Shi, Xinrui Ren, et al.*2026*
26. **[From Domains to Instances: Dual-Granularity Data Synthesis for LLM Unlearning](https://md.research.elamur.ai/free/en/papers/xu-2026-domains-instances-dual-granularity-data/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xiaoyu Xu, Minxin Du, Zitong LI, et al.*2026*
27. **[LOTUS: Evolving Multimodal Unlearning via Hyperbolic Entailment and Lorentz Transport](https://md.research.elamur.ai/free/en/papers/wang-2026-lotus-evolving-multimodal-unlearning-hyperbolic/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zekun Wang, Jingjie Zeng, Yingxu Li, et al.*2026*
28. **[Representation-Guided Parameter-Efficient LLM Unlearning](https://md.research.elamur.ai/free/en/papers/xiao-2026-representation-guided-parameter-efficient-llm/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zeguan Xiao, Lang Mo, Yun Chen, et al.*2026*
29. **[The Impact of Off-Policy Training Data on Probe Generalisation](https://md.research.elamur.ai/free/en/papers/skapars-2026-impact-off-policy-training-data/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Adrians Skapars, Nathalie Maria Kirch, Samuel Dower, et al.*2026*

85 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/li-2024-wmdp-benchmark-measuring-reducing-malicious/) — The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning