mlsanityData to 7 Oct 2026

Dual Use Concerns of Generative AI and Large Language Models

arXiv 2305.07882 ↗First posted 13 May 2023cs.CY cs.CLcited by 6

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Abstract (arXiv)

We suggest the implementation of the Dual Use Research of Concern (DURC) framework, originally designed for life sciences, to the domain of generative AI, with a specific focus on Large Language Models (LLMs). With its demonstrated advantages and drawbacks in biological research, we believe the DURC criteria can be effectively redefined for LLMs, potentially contributing to improved AI governance. Acknowledging the balance that must be struck when employing the DURC framework, we highlight its crucial political role in enhancing societal awareness of the impact of generative AI. As a final point, we offer a series of specific recommendations for applying the DURC approach to LLM research.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 19 of its 93 references that are in the map. First posted on arXiv 13 May 2023.

Builds on

19 of 93 references.

References · 19

  1. 2021On the Opportunities and Risks of Foundation Modelscited by 3,961
  2. 2022Emergent Abilities of Large Language Modelscited by 2,483
  3. 2021Ethical and social risks of harm from Language Modelscited by 957
  4. 2016Semantics derived automatically from language corpora contain human-like biasescited by 894
  5. 2023LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attentioncited by 776
  6. 2023A Watermark for Large Language Modelscited by 667
  7. 2023Can AI-Generated Text be Reliably Detected?cited by 372
  8. 2021All That's 'Human' Is Not Gold: Evaluating Human Evaluation of Generated Textcited by 287
  9. 2022Quantifying Privacy Risks of Masked Language Models Using Membership Inference Attackscited by 168
  10. 2018The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigationcited by 159
  11. 2021Understanding the Capabilities, Limitations, and Societal Impact of Large Language Modelscited by 159
  12. 2021Evaluating the Robustness of Neural Language Models to Input Perturbationscited by 87
7 more
  1. 2021A Survey of Race, Racism, and Anti-Racism in NLPcited by 74
  2. 2021Spinning Language Models: Risks of Propaganda-As-A-Service and Countermeasurescited by 69
  3. 2022Perturbation Augmentation for Fairer NLPcited by 51
  4. 2020Attacking Neural Text Detectorscited by 37
  5. 2022The Ethical Need for Watermarks in Machine-Generated Languagecited by 30
  6. 2021Copyright in Generative Deep Learningcited by 29
  7. 2022Fine-Tuning Language Models via Epistemic Neural Networkscited by 22

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 3; 2026: 1.

6 papers.

  1. 2024Responsible Retrieval Augmented Generation for Climate Decision Making from Documentscited by 4
  2. 2024A University Framework for the Responsible use of Generative AI in Researchcited by 1
  3. 2025Dual use issues in the field of Natural Language Generationcited by 1
  4. 2025Can Large Language Models Design Biological Weapons? Evaluating Moremi Biocited by 1
  5. 2026David vs. Goliath: Verifiable Agent-to-Agent Jailbreaking via Reinforcement Learningcited by 1
  6. 2025Large Language Models in Cybersecurity: Applications, Vulnerabilities, and Defense Techniquescited by 0