mlsanityData to 7 Oct 2026

Understanding the Capabilities, Limitations, and Societal Impact of Large Language Models

arXiv 2102.02503 ↗First posted 4 Feb 2021cs.CL cs.LGcited by 159

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Abstract (arXiv)

On October 14th, 2020, researchers from OpenAI, the Stanford Institute for Human-Centered Artificial Intelligence, and other universities convened to discuss open research questions surrounding GPT-3, the largest publicly-disclosed dense language model at the time. The meeting took place under Chatham House Rules. Discussants came from a variety of research backgrounds including computer science, linguistics, philosophy, political science, communications, cyber policy, and more. Broadly, the discussion centered around two main questions: 1) What are the technical capabilities and limitations of large language models? 2) What are the societal effects of widespread use of large language models? Here, we provide a detailed summary of the discussion organized by the two themes above.

Key facts. Cited by 159 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Feb 2021.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 10; 2022: 17; 2023: 58; 2024: 43; 2025: 21; 2026: 10.

159 papers.

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  1. 2022Training language models to follow instructions with human feedbackcited by 15,712
  2. 2024GPT-4o System Cardcited by 4,467
  3. 2021TruthfulQA: Measuring How Models Mimic Human Falsehoodscited by 2,919
  4. 2023A Survey of Large Language Modelscited by 2,537
  5. 2023CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Societycited by 1,348
  6. 2021Ethical and social risks of harm from Language Modelscited by 957
  7. 2022Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learnedcited by 702
  8. 2023Towards Measuring the Representation of Subjective Global Opinions in Language Modelscited by 424
  9. 2023Large Language Model Alignment: A Surveycited by 241
  10. 2023Large Language Models in Finance: A Surveycited by 223
  11. 2021Societal Biases in Language Generation: Progress and Challengescited by 201
  12. 2023Okapi: Instruction-tuned Large Language Models in Multiple Languages with Reinforcement Learning from Human Feedbackcited by 189
38 more
  1. 2023Survey of Vulnerabilities in Large Language Models Revealed by Adversarial Attackscited by 189
  2. 2023CulturaX: A Cleaned, Enormous, and Multilingual Dataset for Large Language Models in 167 Languagescited by 172
  3. 2021Alignment of Language Agentscited by 167
  4. 2023In-Context Impersonation Reveals Large Language Models' Strengths and Biasescited by 154
  5. 2022MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoningcited by 153
  6. 2023Auditing large language models: a three-layered approachcited by 135
  7. 2022Evaluating and Inducing Personality in Pre-trained Language Modelscited by 122
  8. 2023Opportunities and Challenges for ChatGPT and Large Language Models in Biomedicine and Healthcited by 118
  9. 2022The Ghost in the Machine has an American accent: value conflict in GPT-3cited by 105
  10. 2023How Many Unicorns Are in This Image? A Safety Evaluation Benchmark for Vision LLMscited by 103
  11. 2022Programming Is Hard -- Or at Least It Used to Be: Educational Opportunities And Challenges of AI Code Generationcited by 97
  12. 2022Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural Languagecited by 81
  13. 2023Towards Mitigating Hallucination in Large Language Models via Self-Reflectioncited by 81
  14. 2023Training Socially Aligned Language Models on Simulated Social Interactionscited by 77
  15. 2021What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformerscited by 75
  16. 2024CLIcK: A Benchmark Dataset of Cultural and Linguistic Intelligence in Koreancited by 65
  17. 2022In conversation with Artificial Intelligence: aligning language models with human valuescited by 61
  18. 2024CogBench: a large language model walks into a psychology labcited by 60
  19. 2024Factual Confidence of LLMs: on Reliability and Robustness of Current Estimatorscited by 59
  20. 2024AI Suggestions Homogenize Writing Toward Western Styles and Diminish Cultural Nuancescited by 59
  21. 2023On the Creativity of Large Language Modelscited by 57
  22. 2023A Short Survey of Viewing Large Language Models in Legal Aspectcited by 51
  23. 2023From Instructions to Intrinsic Human Values -- A Survey of Alignment Goals for Big Modelscited by 45
  24. 2021GitTables: A Large-Scale Corpus of Relational Tablescited by 44
  25. 2024Operationalizing a Threat Model for Red-Teaming Large Language Models (LLMs)cited by 44
  26. 2024Natural Language Processing for the Legal Domain: A Survey of Tasks, Datasets, Models, and Challengescited by 42
  27. 2024Compositional Generative Modeling: A Single Model is Not All You Needcited by 41
  28. 2023Look Before You Leap: An Exploratory Study of Uncertainty Measurement for Large Language Modelscited by 40
  29. 2021The Dangers of Underclaiming: Reasons for Caution When Reporting How NLP Systems Failcited by 38
  30. 2023When does In-context Learning Fall Short and Why? A Study on Specification-Heavy Taskscited by 37
  31. 2023Beyond the Safeguards: Exploring the Security Risks of ChatGPTcited by 36
  32. 2023PTW: Pivotal Tuning Watermarking for Pre-Trained Image Generatorscited by 34
  33. 2025Reinforcement Learning Meets Large Language Models: A Survey of Advancements and Applications Across the LLM Lifecyclecited by 34
  34. 2022Active Learning Helps Pretrained Models Learn the Intended Taskcited by 33
  35. 2023Improving In-context Learning of Multilingual Generative Language Models with Cross-lingual Alignmentcited by 33
  36. 2022Large Language Models can Implement Policy Iterationcited by 31
  37. 2024How Far Are We From AGI: Are LLMs All We Need?cited by 31
  38. 2024AI Safety in Generative AI Large Language Models: A Surveycited by 31

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