mlsanityData to 14 Oct 2026

Ethics Whitepaper: Whitepaper on Ethical Research into Large Language Models

arXiv 2410.19812 ↗First posted 17 Oct 2024cs.CY cs.CLcited by 1

Not on the map. Why?

Abstract (arXiv)

This whitepaper offers an overview of the ethical considerations surrounding research into or with large language models (LLMs). As LLMs become more integrated into widely used applications, their societal impact increases, bringing important ethical questions to the forefront. With a growing body of work examining the ethical development, deployment, and use of LLMs, this whitepaper provides a comprehensive and practical guide to best practices, designed to help those in research and in industry to uphold the highest ethical standards in their work.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 132 of its 271 references that are in the map. First posted on arXiv 17 Oct 2024.

Builds on

132 of 271 references.

References · 132

  1. 2020Extracting Training Data from Large Language Modelscited by 2,189
  2. 2018Datasheets for Datasetscited by 1,386
  3. 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddingscited by 1,312
  4. 2019Energy and Policy Considerations for Deep Learning in NLPcited by 1,254
  5. 2018Model Cards for Model Reportingcited by 1,080
  6. 2021A General Language Assistant as a Laboratory for Alignmentcited by 1,055
  7. 2022Red Teaming Language Models with Language Modelscited by 1,026
  8. 2020Beyond Accuracy: Behavioral Testing of NLP models with CheckListcited by 1,006
  9. 2021Ethical and social risks of harm from Language Modelscited by 966
  10. 2020Language (Technology) is Power: A Critical Survey of "Bias" in NLPcited by 956
  11. 2016Semantics derived automatically from language corpora contain human-like biasescited by 901
  12. 2023Can Large Language Models Be an Alternative to Human Evaluations?cited by 734
38 more
  1. 2020Aligning AI With Shared Human Valuescited by 723
  2. 2023Bias and Fairness in Large Language Models: A Surveycited by 639
  3. 2024OLMo: Accelerating the Science of Language Modelscited by 613
  4. 2019Quantifying the Carbon Emissions of Machine Learningcited by 521
  5. 2019Release Strategies and the Social Impacts of Language Modelscited by 513
  6. 2021Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Surveycited by 463
  7. 2020Artificial Intelligence, Values and Alignmentcited by 448
  8. 2019On Measuring Social Biases in Sentence Encoderscited by 398
  9. 2020Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditingcited by 384
  10. 2019Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Settingcited by 349
  11. 2021AI and the Everything in the Whole Wide World Benchmarkcited by 349
  12. 2021Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLPcited by 337
  13. 2019Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Themcited by 268
  14. 2022Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Modelcited by 265
  15. 2021Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detectioncited by 264
  16. 2023AI Alignment: A Comprehensive Surveycited by 263
  17. 2020Data and its (dis)contents: A survey of dataset development and use in machine learning researchcited by 260
  18. 2023From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Modelscited by 260
  19. 2020Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learningcited by 249
  20. 2024LLMs instead of Human Judges? A Large Scale Empirical Study across 20 NLP Evaluation Taskscited by 243
  21. 2021Latent Hatred: A Benchmark for Understanding Implicit Hate Speechcited by 217
  22. 2020HateCheck: Functional Tests for Hate Speech Detection Modelscited by 216
  23. 2019Probing Neural Network Comprehension of Natural Language Argumentscited by 214
  24. 2023Language Model Tokenizers Introduce Unfairness Between Languagescited by 214
  25. 2019Racial Bias in Hate Speech and Abusive Language Detection Datasetscited by 209
  26. 2022Are Large Pre-Trained Language Models Leaking Your Personal Information?cited by 200
  27. 2019A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cyclecited by 193
  28. 2023Power Hungry Processing: Watts Driving the Cost of AI Deployment?cited by 191
  29. 2022The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrinkcited by 184
  30. 2024A Roadmap to Pluralistic Alignmentcited by 184
  31. 2020Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthycited by 183
  32. 2023GPT detectors are biased against non-native English writerscited by 178
  33. 2021The Values Encoded in Machine Learning Researchcited by 172
  34. 2020Towards Debiasing Sentence Representationscited by 171
  35. 2021Challenges in Detoxifying Language Modelscited by 169
  36. 2020Characterising Bias in Compressed Modelscited by 167
  37. 2023Sociotechnical Safety Evaluation of Generative AI Systemscited by 157
  38. 2021RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Modelscited by 149

50 most cited of 132 All 132 (JSON)

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Towards Alignment-Centric Paradigm: A Survey of Instruction Tuning in Large Language Modelscited by 5