mlsanityData to 14 Oct 2026

Frontier AI Ethics: Anticipating and Evaluating the Societal Impacts of Language Model Agents

arXiv 2404.06750 ↗First posted 10 Apr 2024cs.CY cs.AIcited by 5

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

Some have criticised Generative AI Systems for replicating the familiar pathologies of already widely-deployed AI systems. Other critics highlight how they foreshadow vastly more powerful future systems, which might threaten humanity's survival. The first group says there is nothing new here; the other looks through the present to a perhaps distant horizon. In this paper, I instead pay attention to what makes these particular systems distinctive: both their remarkable scientific achievement, and the most likely and consequential ways in which they will change society over the next five to ten years. In particular, I explore the potential societal impacts and normative questions raised by the looming prospect of 'Language Model Agents', in which multimodal large language models (LLMs) form the executive centre of complex, tool-using AI systems that can take unsupervised sequences of actions towards some goal.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 34 of its 47 references that are in the map. First posted on arXiv 10 Apr 2024.

Builds on

34 of 47 references.

References · 34

  1. 2017Attention Is All You Needcited by 60,361
  2. 2022Training language models to follow instructions with human feedbackcited by 15,971
  3. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  4. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  5. 2021On the Opportunities and Risks of Foundation Modelscited by 4,033
  6. 2017Deep reinforcement learning from human preferencescited by 3,912
  7. 2022Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackcited by 3,720
  8. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,211
  9. 2022Constitutional AI: Harmlessness from AI Feedbackcited by 3,050
  10. 2022Emergent Abilities of Large Language Modelscited by 2,512
  11. 2023Voyager: An Open-Ended Embodied Agent with Large Language Modelscited by 2,191
  12. 2023WebArena: A Realistic Web Environment for Building Autonomous Agentscited by 2,006
22 more
  1. 2023Mind2Web: Towards a Generalist Agent for the Webcited by 1,242
  2. 2020Aligning AI With Shared Human Valuescited by 723
  3. 2021Persistent Anti-Muslim Bias in Large Language Modelscited by 377
  4. 2023On the Planning Abilities of Large Language Models : A Critical Investigationcited by 312
  5. 2023An Overview of Catastrophic AI Riskscited by 222
  6. 2024OS-Copilot: Towards Generalist Computer Agents with Self-Improvementcited by 205
  7. 2021Process for Adapting Language Models to Society (PALMS) with Values-Targeted Datasetscited by 181
  8. 2023The Capacity for Moral Self-Correction in Large Language Modelscited by 173
  9. 2023Removing RLHF Protections in GPT-4 via Fine-Tuningcited by 171
  10. 2024Teaching Large Language Models to Reason with Reinforcement Learningcited by 160
  11. 2021Can Machines Learn Morality? The Delphi Experimentcited by 141
  12. 2024Beyond A*: Better Planning with Transformers via Search Dynamics Bootstrappingcited by 90
  13. 2024Can Large Language Models Reason and Plan?cited by 82
  14. 2024Functional Benchmarks for Robust Evaluation of Reasoning Performance, and the Reasoning Gapcited by 79
  15. 2024Scaling Instructable Agents Across Many Simulated Worldscited by 71
  16. 2023Leveraging Large Language Models in Conversational Recommender Systemscited by 63
  17. 2023Rewarding Chatbots for Real-World Engagement with Millions of Userscited by 59
  18. 2023ToolTalk: Evaluating Tool-Usage in a Conversational Settingcited by 45
  19. 2023ProAgent: From Robotic Process Automation to Agentic Process Automationcited by 26
  20. 2024TOOLVERIFIER: Generalization to New Tools via Self-Verificationcited by 24
  21. 2022Building Human Values into Recommender Systems: An Interdisciplinary Synthesiscited by 22
  22. 2022Legitimacy, Authority, and Democratic Duties of Explanationcited by 4

Built on by

Cited by year (2026 to 14 Oct)

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

5 papers.

  1. 2025Discerning What Matters: A Multi-Dimensional Assessment of Moral Competence in LLMscited by 10
  2. 2025Position: Stop Acting Like Language Model Agents Are Normal Agentscited by 5
  3. 2026Architecting Trust in Artificial Epistemic Agentscited by 3
  4. 2024Large Language Models, and LLM-Based Agents, Should Be Used to Enhance the Digital Public Spherecited by 1
  5. 2026The Agentic Web Requires New Normative Infrastructurecited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Do Large Language Models Solve the Problems of Agent-Based Modeling? A Critical Review of Generative Social Simulations
  2. 2024A Moral Imperative: The Need for Continual Superalignment of Large Language Models
  3. 2023The Rise and Potential of Large Language Model Based Agents: A Survey
  4. 2026Toward Human-Centered Multi-Agent Systems: Integrating Cognition, Culture, Values, and Cooperation in AI Agents
  5. 2025Small Language Models are the Future of Agentic AI
  6. 2025Beyond Static Responses: Multi-Agent LLM Systems as a New Paradigm for Social Science Research
  7. 2026Questionnaire Responses Do not Capture the Safety of AI Agents
  8. 2025From Language to Action: A Review of Large Language Models as Autonomous Agents and Tool Users